Bone proteins are associated with cardiovascular risk according to the SCORE2-Diabetes algorithm

Typical bone proteins, such as sclerostin and periostin, have been associated with cardiovascular disease (CVD). Simultaneously, several risk scores have been developed to predict CVD in the general population. Therefore, we aimed to evaluate the association of these bone proteins related to CVD, with the main vascular risk scales: Framingham Risk Score (FRS), REGICOR and SCORE2-Diabetes, in patients with type 2 diabetes. We focus in particular on the SCORE2-Diabetes algorithm, which predicts 10-year CVD risk and is specific to the study population.

This was a cross-sectional study including 104 patients with type 2 diabetes (62 ± 6 years, 60% males). Clinical data, biochemical measurements, and serum bioactive sclerostin and periostin levels were collected, and different risk scales were calculated. The association between bioactive sclerostin or periostin with the risk scales was analyzed.

A positive correlation was observed between circulating levels of bioactive sclerostin (p < 0.001) and periostin (p < 0.001) with SCORE2-Diabetes values. However, no correlation was found with FRS or REGICOR scales. Both serum bioactive sclerostin and periostin levels were significantly elevated in patients at high-very high risk of CVD (score ≥ 10%) than in the low-moderate risk group (score < 10%) (p < 0.001 for both). Moreover, analyzing these proteins to identify patients with type 2 diabetes at high-very high vascular risk using ROC curves, we observed significant AUC values for bioactive sclerostin (AUC = 0.696; p = 0.001), periostin (AUC = 0.749; p < 0.001), and the model combining both (AUC = 0.795; p < 0.001). For diagnosing high-very high vascular risk, serum bioactive sclerostin levels > 131 pmol/L showed 51.6% sensitivity and 78.6% specificity. Similarly, serum periostin levels > 1144 pmol/L had 64.5% sensitivity and 76.2% specificity.

Sclerostin and periostin are associated with vascular risk in the SCORE2-Diabetes algorithm, opening a new line of investigation to identify novel biomarkers of cardiovascular risk in the type 2 diabetes population.