Bibliographic Details
| Title: |
Predicting the 10-year incidence of dyslipidemia based on novel anthropometric indices, using data mining. |
| Authors: |
Takhttavous, Alireza1,2 (AUTHOR), Saberi-Karimian, Maryam3,4 (AUTHOR), Hafezi, Somayeh Ghiasi3 (AUTHOR), Esmaily, Habibollah5,6 (AUTHOR), Hosseini, Marzieh7 (AUTHOR), Ferns, Gordon A.8 (AUTHOR), Amirfakhrian, Elham3 (AUTHOR), Ghamsary, Mark9 (AUTHOR) mghamsary@gmail.com, Ghayour-Mobarhan, Majid2,3 (AUTHOR) ghayourm@mums.ac.ir, Alinezhad-Namaghi, Maryam10 (AUTHOR) Alinezhadnm@mums.ac.ir |
| Superior Title: |
Lipids in Health & Disease. 1/31/2024, Vol. 23 Issue 1, p1-11. 11p. |
| Subject Terms: |
*DATA mining, *DYSLIPIDEMIA, *BODY surface area, *RECEIVER operating characteristic curves, *BODY mass index |
| Abstract: |
Background: The aim was to establish a 10-year dyslipidemia incidence model, investigating novel anthropometric indices using exploratory regression and data mining. Methods: This data mining study was conducted on people who were diagnosed with dyslipidemia in phase 2 (n = 1097) of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study, who were compared with healthy people in this phase (n = 679). The association of dyslipidemia with several novel anthropometric indices including Conicity Index (C-Index), Body Roundness Index (BRI), Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), Abdominal Volume Index (AVI), Weight-Adjusted-Waist Index (WWI), A Body Shape Index (ABSI), Body Mass Index (BMI), Body Adiposity Index (BAI) and Body Surface Area (BSA) was evaluated. Logistic Regression (LR) and Decision Tree (DT) analysis were utilized to evaluate the association. The accuracy, sensitivity, and specificity of DT were assessed through the performance of a Receiver Operating Characteristic (ROC) curve using R software. Results: A total of 1776 subjects without dyslipidemia during phase 1 were followed up in phase 2 and enrolled into the current study. The AUC of models A and B were 0.69 and 0.63 among subjects with dyslipidemia, respectively. VAI has been identified as a significant predictor of dyslipidemias (OR: 2.81, (95% CI: 2.07, 3.81)) in all models. Moreover, the DT showed that VAI followed by BMI and LAP were the most critical variables in predicting dyslipidemia incidence. Conclusions: Based on the results, model A had an acceptable performance for predicting 10 years of dyslipidemia incidence. Furthermore, the VAI, BMI, and LAP were the principal anthropometric factors for predicting dyslipidemia incidence by LR and DT models. [ABSTRACT FROM AUTHOR] |
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