Prediction of insulin resistance using multiple adaptive regression spline in Chinese women
Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical tool for quantifying insulin resistance (HOMA-IR). Multiple adaptive regression spline (MARS) is a machine learning method used in many research fields...
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| Format: | Article |
| Language: | English |
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The Japan Endocrine Society
2025-04-01
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| Series: | Endocrine Journal |
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| Online Access: | https://www.jstage.jst.go.jp/article/endocrj/72/4/72_EJ24-0449/_html/-char/en |
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| author | Shih-Peng Mao Chen-Yu Wang Chi-Hao Liu Chung-Bao Hsieh Dee Pei Ta-Wei Chu Yao-Jen Liang |
| author_facet | Shih-Peng Mao Chen-Yu Wang Chi-Hao Liu Chung-Bao Hsieh Dee Pei Ta-Wei Chu Yao-Jen Liang |
| author_sort | Shih-Peng Mao |
| collection | DOAJ |
| description | Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical tool for quantifying insulin resistance (HOMA-IR). Multiple adaptive regression spline (MARS) is a machine learning method used in many research fields but has yet to be applied to estimating HOMA-IR. This study uses MARS to build an equation to estimate HOMA-IR in pre-menopausal Chinese women based on a sample of 4,071 healthy women aged 20–50 with no major diseases and no medication use for blood pressure, blood glucose or blood lipids. Thirty variables were applied to build the HOMA-IR model, including demographic, laboratory, and lifestyle factors. MARS results in smaller prediction errors than traditional multiple linear regression (MLR) methods, and is thus more accurate. The model was established based on key impact factors including waist-hip ratio (WHR), C reactive protein (CRP), uric acid (UA), total bilirubin (TBIL), leukocyte (WBC), serum glutamic oxaloacetic transaminase (GOT), high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), serum glutamic pyruvic transaminase (GPT), and triglycerides (TG). The equation is as following: HOMA-IR = 6.634 – 1.448MAX(0, 0.833 – WHR) + 10.152MAX(0, WHR – 0.833) – 1.351MAX(0, 0.7 – CRP) – 0.449MAX(0, CRP – 0.7) + 1.062MAX(0, UA – 8.5) + +1.047(MAX(0, 0.83 – TBIL) + 0.681MAX(0, WBC – 11.53) – 0.071MAX(0, 11.53 – WBC) + 0.043MAX(0, 24 – GOT) – 0.017MAX(0, GOT – 24) + 0.021MAX(0, 59 – HDL) – 0.005MAX(0, HDL – 59) – 0.013MAX(0, 141 – SBP) – 0.033MAX(0, 100 – GPT) + 0.013MAX(0, GPT – 100) – 0.004MAX(303 – TG) Results indicate that MARS is a more precise tool than fasting plasma insulin (FPI) levels, and could be used in the daily practice, and further longitudinal studies are warranted. |
| format | Article |
| id | doaj-art-3ca03edc6d134f86813dc8d3653b79a8 |
| institution | DOAJ |
| issn | 1348-4540 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | The Japan Endocrine Society |
| record_format | Article |
| series | Endocrine Journal |
| spelling | doaj-art-3ca03edc6d134f86813dc8d3653b79a82025-08-20T02:53:41ZengThe Japan Endocrine SocietyEndocrine Journal1348-45402025-04-0172438739810.1507/endocrj.EJ24-0449endocrjPrediction of insulin resistance using multiple adaptive regression spline in Chinese womenShih-Peng Mao0Chen-Yu Wang1Chi-Hao Liu2Chung-Bao Hsieh3Dee Pei4Ta-Wei Chu5Yao-Jen Liang6Department of Obstetrics and Gynecology, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235, Taiwan, R.O.C.Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan, R.O.C.Division of Nephrology, Department of Medicine, Kaohsiung Armed Forces General Hospital, Kaohsiung 802, Taiwan, R.O.C.Department of Surgery, Kaohsiung Armed Forces General Hospital, Kaohsiung 802, Taiwan, R.O.C.Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 242, Taiwan, R.O.C.Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan, R.O.C.Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan, R.O.C.Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical tool for quantifying insulin resistance (HOMA-IR). Multiple adaptive regression spline (MARS) is a machine learning method used in many research fields but has yet to be applied to estimating HOMA-IR. This study uses MARS to build an equation to estimate HOMA-IR in pre-menopausal Chinese women based on a sample of 4,071 healthy women aged 20–50 with no major diseases and no medication use for blood pressure, blood glucose or blood lipids. Thirty variables were applied to build the HOMA-IR model, including demographic, laboratory, and lifestyle factors. MARS results in smaller prediction errors than traditional multiple linear regression (MLR) methods, and is thus more accurate. The model was established based on key impact factors including waist-hip ratio (WHR), C reactive protein (CRP), uric acid (UA), total bilirubin (TBIL), leukocyte (WBC), serum glutamic oxaloacetic transaminase (GOT), high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), serum glutamic pyruvic transaminase (GPT), and triglycerides (TG). The equation is as following: HOMA-IR = 6.634 – 1.448MAX(0, 0.833 – WHR) + 10.152MAX(0, WHR – 0.833) – 1.351MAX(0, 0.7 – CRP) – 0.449MAX(0, CRP – 0.7) + 1.062MAX(0, UA – 8.5) + +1.047(MAX(0, 0.83 – TBIL) + 0.681MAX(0, WBC – 11.53) – 0.071MAX(0, 11.53 – WBC) + 0.043MAX(0, 24 – GOT) – 0.017MAX(0, GOT – 24) + 0.021MAX(0, 59 – HDL) – 0.005MAX(0, HDL – 59) – 0.013MAX(0, 141 – SBP) – 0.033MAX(0, 100 – GPT) + 0.013MAX(0, GPT – 100) – 0.004MAX(303 – TG) Results indicate that MARS is a more precise tool than fasting plasma insulin (FPI) levels, and could be used in the daily practice, and further longitudinal studies are warranted.https://www.jstage.jst.go.jp/article/endocrj/72/4/72_EJ24-0449/_html/-char/eninsulin resistancehomeostasis assessment modelmultiple adaptive regression spline |
| spellingShingle | Shih-Peng Mao Chen-Yu Wang Chi-Hao Liu Chung-Bao Hsieh Dee Pei Ta-Wei Chu Yao-Jen Liang Prediction of insulin resistance using multiple adaptive regression spline in Chinese women Endocrine Journal insulin resistance homeostasis assessment model multiple adaptive regression spline |
| title | Prediction of insulin resistance using multiple adaptive regression spline in Chinese women |
| title_full | Prediction of insulin resistance using multiple adaptive regression spline in Chinese women |
| title_fullStr | Prediction of insulin resistance using multiple adaptive regression spline in Chinese women |
| title_full_unstemmed | Prediction of insulin resistance using multiple adaptive regression spline in Chinese women |
| title_short | Prediction of insulin resistance using multiple adaptive regression spline in Chinese women |
| title_sort | prediction of insulin resistance using multiple adaptive regression spline in chinese women |
| topic | insulin resistance homeostasis assessment model multiple adaptive regression spline |
| url | https://www.jstage.jst.go.jp/article/endocrj/72/4/72_EJ24-0449/_html/-char/en |
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