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Yousefabadi SA, Ghiasi Hafezi S, Kooshki A, Hosseini M, Mansoori A, Ghamsary M, Esmaily H, Ghayour-Mobarhan M. Evaluating the Association of Anthropometric Indices With Total Cholesterol in a Large Population Using Data Mining Algorithms. J Clin Lab Anal 2024:e25095. [PMID: 39269036 DOI: 10.1002/jcla.25095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 08/09/2024] [Accepted: 08/10/2024] [Indexed: 09/15/2024] Open
Abstract
BACKGROUND Unbalanced levels of serum total cholesterol (TC) and its subgroups are called dyslipidemia. Several anthropometric indices have been developed to provide a more accurate assessment of body shape and the health risks associated with obesity. In this study, we used the random forest model (RF), decision tree (DT), and logistic regression (LR) to predict total cholesterol based on new anthropometric indices in a sex-stratified analysis. METHOD Our sample size was 9639 people in which anthropometric parameters were measured for the participants and data regarding the demographic and laboratory data were obtained. Aiding the machine learning, DT, LR, and RF were drawn to build a measurement prediction model. RESULTS Anthropometric and other related variables were compared between both TC <200 and TC ≥200 groups. In both males and females, Lipid Accumulation Product (LAP) had the greatest effect on the risk of TC increase. According to results of the RF model, LAP and Visceral Adiposity Index (VAI) were significant variables for men. VAI also had a stronger correlation with HDL-C and triglyceride. We identified specific anthropometric thresholds based on DT analysis that could be used to classify individuals at high or low risk of elevated TC levels. The RF model determined that the most important variables for both genders were VAI and LAP. CONCLUSION We tend to present a picture of the Persian population's anthropometric factors and their association with TC level and possible risk factors. Various anthropometric indices indicated different predictive power for TC levels in the Persian population.
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Affiliation(s)
- Sahar Arab Yousefabadi
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Somayeh Ghiasi Hafezi
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Kooshki
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Marzieh Hosseini
- Department of Biostatistics, College of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Amin Mansoori
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mark Ghamsary
- School of Public Health, Loma Linda University, Loma Linda, California, USA
| | - Habibollah Esmaily
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
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Takhttavous A, Saberi-Karimian M, Hafezi SG, Esmaily H, Hosseini M, Ferns GA, Amirfakhrian E, Ghamsary M, Ghayour-Mobarhan M, Alinezhad-Namaghi M. Predicting the 10-year incidence of dyslipidemia based on novel anthropometric indices, using data mining. Lipids Health Dis 2024; 23:33. [PMID: 38297277 PMCID: PMC10829243 DOI: 10.1186/s12944-024-02006-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/04/2024] [Indexed: 02/02/2024] Open
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.
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Affiliation(s)
- Alireza Takhttavous
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Maryam Saberi-Karimian
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Endoscopic and Minimally Invasive Surgery Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Somayeh Ghiasi Hafezi
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Habibollah Esmaily
- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Marzieh Hosseini
- School of Public Health, Department of Epidemiology and Biostatistics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Gordon A Ferns
- Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex, BN1 9PH, UK
| | - Elham Amirfakhrian
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mark Ghamsary
- School of Public Health, Department of Epidemiology and Biostatistics, Loma Linda University, Loma Linda, USA.
| | - Majid Ghayour-Mobarhan
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Maryam Alinezhad-Namaghi
- Transplant Research Center, Clinical Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
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