Equation Built by Multiple Adaptive Regression Spline to Estimate Biological Age in Healthy Postmenopausal Women in Taiwan
<b>Background:</b> Biological age (BA) is a better representative of health status than chronological age (CA), as it uses different biological markers to quantify cellular and systemic change status. However, BA can be difficult to accurately estimate using current methods. This study u...
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2025-04-01
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| author | Chun-Feng Chang Ta-Wei Chu Chi-Hao Liu Sheng-Tang Wu Chung-Chi Yang |
| author_facet | Chun-Feng Chang Ta-Wei Chu Chi-Hao Liu Sheng-Tang Wu Chung-Chi Yang |
| author_sort | Chun-Feng Chang |
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| description | <b>Background:</b> Biological age (BA) is a better representative of health status than chronological age (CA), as it uses different biological markers to quantify cellular and systemic change status. However, BA can be difficult to accurately estimate using current methods. This study uses multiple adaptive regression spline (MARS) to build an equation to estimate BA among healthy postmenopausal women, thereby potentially improving the efficiency and accuracy of BA assessment. <b>Methods:</b> A total of 11,837 healthy women were enrolled (≥51 years old), excluding participants with metabolic syndrome variable values outside two standard deviations. MARS was applied, with the results compared to traditional multiple linear regression (MLR). The method with the smaller degree of estimation error was considered to be more accurate. The lower prediction errors yielded by MARS compared to the MLR method suggest that MARS performs better than MLR. <b>Results:</b> The equation derived from MARS is depicted. It could be noted that BA could be determined by marriage, systolic blood pressure (SBP), diastolic blood pressure (DBP), waist–hip ratio (WHR), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), creatinine (Cr), carcinoembryonic antigen (CEA), bone mineral density (BMD), education level, and income. The MARS equation is generated. <b>Conclusions:</b> Using MARS, an equation was built to estimate biological age among healthy postmenopausal women in Taiwan. This equation could be used as a reference for calculating BA in general. Our equation showed that the most important factor was BMD, followed by WHR, Cr, marital status, education level, income, CEA, blood pressure, ALP, and LDH. |
| format | Article |
| id | doaj-art-a1be01d421d14280824f460d78c467d9 |
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| spelling | doaj-art-a1be01d421d14280824f460d78c467d92025-08-20T01:49:20ZengMDPI AGDiagnostics2075-44182025-04-01159114710.3390/diagnostics15091147Equation Built by Multiple Adaptive Regression Spline to Estimate Biological Age in Healthy Postmenopausal Women in TaiwanChun-Feng Chang0Ta-Wei Chu1Chi-Hao Liu2Sheng-Tang Wu3Chung-Chi Yang4Division of Urology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114202, TaiwanDepartment of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114202, TaiwanDivision of Nephrology, Department of Internal Medicine, Kaohsiung Armed Forces General Hospital, Kaohsiung 802301, TaiwanDivision of Urology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114202, TaiwanDivision of Cardiology, Department of Medicine, Taoyuan Armed Forces General Hospital, Taoyuan 325208, Taiwan<b>Background:</b> Biological age (BA) is a better representative of health status than chronological age (CA), as it uses different biological markers to quantify cellular and systemic change status. However, BA can be difficult to accurately estimate using current methods. This study uses multiple adaptive regression spline (MARS) to build an equation to estimate BA among healthy postmenopausal women, thereby potentially improving the efficiency and accuracy of BA assessment. <b>Methods:</b> A total of 11,837 healthy women were enrolled (≥51 years old), excluding participants with metabolic syndrome variable values outside two standard deviations. MARS was applied, with the results compared to traditional multiple linear regression (MLR). The method with the smaller degree of estimation error was considered to be more accurate. The lower prediction errors yielded by MARS compared to the MLR method suggest that MARS performs better than MLR. <b>Results:</b> The equation derived from MARS is depicted. It could be noted that BA could be determined by marriage, systolic blood pressure (SBP), diastolic blood pressure (DBP), waist–hip ratio (WHR), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), creatinine (Cr), carcinoembryonic antigen (CEA), bone mineral density (BMD), education level, and income. The MARS equation is generated. <b>Conclusions:</b> Using MARS, an equation was built to estimate biological age among healthy postmenopausal women in Taiwan. This equation could be used as a reference for calculating BA in general. Our equation showed that the most important factor was BMD, followed by WHR, Cr, marital status, education level, income, CEA, blood pressure, ALP, and LDH.https://www.mdpi.com/2075-4418/15/9/1147biological agemachine learningpostmenopausalaging |
| spellingShingle | Chun-Feng Chang Ta-Wei Chu Chi-Hao Liu Sheng-Tang Wu Chung-Chi Yang Equation Built by Multiple Adaptive Regression Spline to Estimate Biological Age in Healthy Postmenopausal Women in Taiwan Diagnostics biological age machine learning postmenopausal aging |
| title | Equation Built by Multiple Adaptive Regression Spline to Estimate Biological Age in Healthy Postmenopausal Women in Taiwan |
| title_full | Equation Built by Multiple Adaptive Regression Spline to Estimate Biological Age in Healthy Postmenopausal Women in Taiwan |
| title_fullStr | Equation Built by Multiple Adaptive Regression Spline to Estimate Biological Age in Healthy Postmenopausal Women in Taiwan |
| title_full_unstemmed | Equation Built by Multiple Adaptive Regression Spline to Estimate Biological Age in Healthy Postmenopausal Women in Taiwan |
| title_short | Equation Built by Multiple Adaptive Regression Spline to Estimate Biological Age in Healthy Postmenopausal Women in Taiwan |
| title_sort | equation built by multiple adaptive regression spline to estimate biological age in healthy postmenopausal women in taiwan |
| topic | biological age machine learning postmenopausal aging |
| url | https://www.mdpi.com/2075-4418/15/9/1147 |
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