A predictive model to assess the risk of developing hyperlipidemia in patients with type 2 diabetes.
<h4>Background</h4>Type 2 diabetes (T2D) is increasingly recognized as a significant global health challenge, with a rising prevalence of hyperlipidemia among diabetic patients. Effectively predicting and reducing the risk of hyperlipidemia in T2D patients to mitigate their cardiovascula...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315781 |
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| _version_ | 1850279495748026368 |
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| author | Rujian Ye Xitong Huang Hehui Yang Wei Pan Ping Wang Janhao Men Dawei Huang Shan Wu |
| author_facet | Rujian Ye Xitong Huang Hehui Yang Wei Pan Ping Wang Janhao Men Dawei Huang Shan Wu |
| author_sort | Rujian Ye |
| collection | DOAJ |
| description | <h4>Background</h4>Type 2 diabetes (T2D) is increasingly recognized as a significant global health challenge, with a rising prevalence of hyperlipidemia among diabetic patients. Effectively predicting and reducing the risk of hyperlipidemia in T2D patients to mitigate their cardiovascular risk remains an urgent issue.<h4>Objectives</h4>The research sought to determine early clinical indicators that could predict the onset of hyperlipidemia in patients with T2D and to establish a predictive model that integrates clinical and laboratory indicators.<h4>Methods</h4>A cohort of T2D patients, excluding those with pre-existing hyperlipidemia or confounding factors, was analyzed. Clinical and laboratory data were used in a LASSO regression model to select key predictive variables. A nomogram was then constructed and evaluated using receiver operating characteristic (ROC) analysis and calibration.<h4>Results</h4>Among 269 participants, PCSK9 levels were significantly elevated in T2D patients with hyperlipidemia and exhibited a positive correlation with several lipid markers. LASSO regression identified six predictors: BMI, TG, TC, LDL-C, HbA1c, and PCSK9. The nomogram model exhibited robust predictive performance (AUC, 0.89 (95% CI: 0.802-0.977)) and showed good calibration.<h4>Conclusions</h4>This method effectively predicts the risk of hyperlipidemia in patients with T2D and provides a valuable tool for early intervention. PCSK9, as a key predictor, highlights its potential role in the pathogenesis of diabetes with hyperlipidemia and offers new avenues for targeted therapy. |
| format | Article |
| id | doaj-art-06dd78f2c9ba4c79bf80a6a7eec37ad6 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-06dd78f2c9ba4c79bf80a6a7eec37ad62025-08-20T01:49:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031578110.1371/journal.pone.0315781A predictive model to assess the risk of developing hyperlipidemia in patients with type 2 diabetes.Rujian YeXitong HuangHehui YangWei PanPing WangJanhao MenDawei HuangShan Wu<h4>Background</h4>Type 2 diabetes (T2D) is increasingly recognized as a significant global health challenge, with a rising prevalence of hyperlipidemia among diabetic patients. Effectively predicting and reducing the risk of hyperlipidemia in T2D patients to mitigate their cardiovascular risk remains an urgent issue.<h4>Objectives</h4>The research sought to determine early clinical indicators that could predict the onset of hyperlipidemia in patients with T2D and to establish a predictive model that integrates clinical and laboratory indicators.<h4>Methods</h4>A cohort of T2D patients, excluding those with pre-existing hyperlipidemia or confounding factors, was analyzed. Clinical and laboratory data were used in a LASSO regression model to select key predictive variables. A nomogram was then constructed and evaluated using receiver operating characteristic (ROC) analysis and calibration.<h4>Results</h4>Among 269 participants, PCSK9 levels were significantly elevated in T2D patients with hyperlipidemia and exhibited a positive correlation with several lipid markers. LASSO regression identified six predictors: BMI, TG, TC, LDL-C, HbA1c, and PCSK9. The nomogram model exhibited robust predictive performance (AUC, 0.89 (95% CI: 0.802-0.977)) and showed good calibration.<h4>Conclusions</h4>This method effectively predicts the risk of hyperlipidemia in patients with T2D and provides a valuable tool for early intervention. PCSK9, as a key predictor, highlights its potential role in the pathogenesis of diabetes with hyperlipidemia and offers new avenues for targeted therapy.https://doi.org/10.1371/journal.pone.0315781 |
| spellingShingle | Rujian Ye Xitong Huang Hehui Yang Wei Pan Ping Wang Janhao Men Dawei Huang Shan Wu A predictive model to assess the risk of developing hyperlipidemia in patients with type 2 diabetes. PLoS ONE |
| title | A predictive model to assess the risk of developing hyperlipidemia in patients with type 2 diabetes. |
| title_full | A predictive model to assess the risk of developing hyperlipidemia in patients with type 2 diabetes. |
| title_fullStr | A predictive model to assess the risk of developing hyperlipidemia in patients with type 2 diabetes. |
| title_full_unstemmed | A predictive model to assess the risk of developing hyperlipidemia in patients with type 2 diabetes. |
| title_short | A predictive model to assess the risk of developing hyperlipidemia in patients with type 2 diabetes. |
| title_sort | predictive model to assess the risk of developing hyperlipidemia in patients with type 2 diabetes |
| url | https://doi.org/10.1371/journal.pone.0315781 |
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