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Background <p>The association between rosuvastatin (RST) and the risk of new-onset diabetes mellitus (NODM) is controversial. Although the link between RST and NODM is still debated, there is a lack of effective strategies to predict and prevent potential RST-induced NODM in clinical practice. This study aimed to determine the association between plasma exposure to RST and the risk of developing NODM in patients with cardiovascular disease and to establish predictive models for the early detection of RST-induced NODM.</p> Methods <p>We included 704 patients with cardiovascular disease and without diabetes who had been on RST for &gt; 4 months. We used ultra-performance liquid chromatography–tandem mass spectrometry to detect the concentration of RST and its metabolites. We then used machine learning (ML) to analyze the impact of plasma exposure to RST and patient characteristics on NODM and established four risk prediction models for RST-induced NODM. The optimal model was determined using the receiver-operating-characteristic (ROC) curve (AUC) and the Shapley algorithm (SHAP) interpretation.</p> Results <p>Our findings indicated that high plasma exposure to RST was an independent risk factor for NODM. In terms of NODM, the Random Forest model demonstrated the highest AUC value (0.7310) in the testing set and was validated as the best performing model. The SHAP analysis identified high triglyceride levels, advanced age, and high RST plasma exposure as the top three predictors of NODM. In addition, low total cholesterol and LDL-cholesterol levels were also associated with an increased risk of NODM.</p> Conclusions <p>The study findings revealed high plasma exposure of RST as an independent risk factor for NODM in patients without diabetes who are on RST, which may relate to “on-target” effects of RST. In addition, ML interpreted using SHAP provide valuable tools for early prediction and personalized management of RST-induced NODM.</p>

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Exploring the impact of high rosuvastatin plasma exposure on new-onset diabetes mellitus: insights from machine learning-based prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月25日

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  • Yu Chen,
  • Fanyan Meng,
  • Fang Song,
  • Cailin Tang,
  • Baolin Chen,
  • Shihan Zhao,
  • Zhuoqi Ge,
  • Li Li,
  • Xingzhen Long,
  • Qi Chen,
  • Changcheng Sheng,
  • Qian Mei,
  • Jue Liu,
  • Shilong Zhong,
  • Xue Bai

摘要

Background

The association between rosuvastatin (RST) and the risk of new-onset diabetes mellitus (NODM) is controversial. Although the link between RST and NODM is still debated, there is a lack of effective strategies to predict and prevent potential RST-induced NODM in clinical practice. This study aimed to determine the association between plasma exposure to RST and the risk of developing NODM in patients with cardiovascular disease and to establish predictive models for the early detection of RST-induced NODM.

Methods

We included 704 patients with cardiovascular disease and without diabetes who had been on RST for > 4 months. We used ultra-performance liquid chromatography–tandem mass spectrometry to detect the concentration of RST and its metabolites. We then used machine learning (ML) to analyze the impact of plasma exposure to RST and patient characteristics on NODM and established four risk prediction models for RST-induced NODM. The optimal model was determined using the receiver-operating-characteristic (ROC) curve (AUC) and the Shapley algorithm (SHAP) interpretation.

Results

Our findings indicated that high plasma exposure to RST was an independent risk factor for NODM. In terms of NODM, the Random Forest model demonstrated the highest AUC value (0.7310) in the testing set and was validated as the best performing model. The SHAP analysis identified high triglyceride levels, advanced age, and high RST plasma exposure as the top three predictors of NODM. In addition, low total cholesterol and LDL-cholesterol levels were also associated with an increased risk of NODM.

Conclusions

The study findings revealed high plasma exposure of RST as an independent risk factor for NODM in patients without diabetes who are on RST, which may relate to “on-target” effects of RST. In addition, ML interpreted using SHAP provide valuable tools for early prediction and personalized management of RST-induced NODM.