{"title":"Predicting bone aging using spatially offset Raman spectroscopy: a longitudinal analysis on mice.","authors":"Hongmei Dou, Wendong Sun, Shuo Chen, Keren Chen","doi":"10.1007/s00216-025-05819-x","DOIUrl":null,"url":null,"abstract":"<p><p>Osteoporosis, a global health concern, poses an increasing challenge due to the aging population. While dual-energy X-ray absorptiometry (DXA) scans measuring bone mineral density (BMD) remain the clinical standard for osteoporosis diagnosis, this method's inability to detect changes in bone chemical composition limits its effectiveness in early diagnosis. This study applies Raman spectroscopy on examining bone aging in Senescence Accelerated Mouse Prone 6 (SAMP6) mice compared to their senescence-resistant controls (SAMR1) over an age period from 6 to 10 months. We performed Raman spectroscopic analysis on mouse tibiae both transcutaneously and on exposed bone. Leave-one-out cross-validation combined with partial least squares regression (LOOCV-PLSR) was applied to analyze Raman spectra to predict age, BMD, and maximum torque (MT) as determined by biomechanical testing. Our results revealed significant correlations between Raman spectroscopic predictions and reference values, particularly for age determination. To our knowledge, this study represents the first demonstration of transcutaneous Raman spectroscopy for accurate bone aging prediction, showing a strong correlation with established reference measurements.</p>","PeriodicalId":462,"journal":{"name":"Analytical and Bioanalytical Chemistry","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical and Bioanalytical Chemistry","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1007/s00216-025-05819-x","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Osteoporosis, a global health concern, poses an increasing challenge due to the aging population. While dual-energy X-ray absorptiometry (DXA) scans measuring bone mineral density (BMD) remain the clinical standard for osteoporosis diagnosis, this method's inability to detect changes in bone chemical composition limits its effectiveness in early diagnosis. This study applies Raman spectroscopy on examining bone aging in Senescence Accelerated Mouse Prone 6 (SAMP6) mice compared to their senescence-resistant controls (SAMR1) over an age period from 6 to 10 months. We performed Raman spectroscopic analysis on mouse tibiae both transcutaneously and on exposed bone. Leave-one-out cross-validation combined with partial least squares regression (LOOCV-PLSR) was applied to analyze Raman spectra to predict age, BMD, and maximum torque (MT) as determined by biomechanical testing. Our results revealed significant correlations between Raman spectroscopic predictions and reference values, particularly for age determination. To our knowledge, this study represents the first demonstration of transcutaneous Raman spectroscopy for accurate bone aging prediction, showing a strong correlation with established reference measurements.
期刊介绍:
Analytical and Bioanalytical Chemistry’s mission is the rapid publication of excellent and high-impact research articles on fundamental and applied topics of analytical and bioanalytical measurement science. Its scope is broad, and ranges from novel measurement platforms and their characterization to multidisciplinary approaches that effectively address important scientific problems. The Editors encourage submissions presenting innovative analytical research in concept, instrumentation, methods, and/or applications, including: mass spectrometry, spectroscopy, and electroanalysis; advanced separations; analytical strategies in “-omics” and imaging, bioanalysis, and sampling; miniaturized devices, medical diagnostics, sensors; analytical characterization of nano- and biomaterials; chemometrics and advanced data analysis.