{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T07:17:59Z","timestamp":1788247079153,"version":"build-2803163510"},"reference-count":38,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Guangci Innovative Technology Program","award":["KY2023810"],"award-info":[{"award-number":["KY2023810"]}]},{"name":"Guangci Talent Program","award":["RC20240018"],"award-info":[{"award-number":["RC20240018"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFC3014500"],"award-info":[{"award-number":["2024YFC3014500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72404158"],"award-info":[{"award-number":["72404158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Leader Project of the Oriental Talent Program in 2022","award":["no. 153"],"award-info":[{"award-number":["no. 153"]}]},{"name":"Shanghai Shen-Kang projects","award":["SHDC12024109"],"award-info":[{"award-number":["SHDC12024109"]}]},{"name":"Shanghai Shen-Kang projects","award":["SHDC22022301"],"award-info":[{"award-number":["SHDC22022301"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Frequent glucose monitoring is essential for diabetes management, yet non-invasive approaches have been limited by poor generalizability across patients. This study aimed to evaluate whether a depth-resolved Raman spectroscopy approach can provide consistent glucose prediction accuracy across physiological variability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We enrolled 200 patients with type 2 diabetes and acquired depth-resolved Raman spectra using a multiple \u03bc-spatially offset Raman spectroscopy (m\u03bcSORS) system. An overall partial least squares (PLS) model was developed using 4768 paired venous plasma glucose and concatenated spectra at 100 \u03bcm (offset 2) and 150 \u03bcm (offset 3), and validated by subject-wise tenfold cross-validation. Model robustness was assessed through stratified analyses across physical characteristics, skin parameters, and serum biomarkers, with size-matched comparisons to control for sample size effects. Prediction accuracy was evaluated using mean absolute relative difference (MARD), root mean square error (RMSE), and consensus error grid (CEG).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      The dataset covered a wide glucose range (3.8-31.6 mmol\/L; 68.4-568.8 mg\/dL) and substantial physiological heterogeneity. The overall PLS model achieved a MARD of 14.5% (CEG A + B: 99.4%), with no significant differences across stratified subgroups (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &gt; .05). Stratified subgroup models performed significantly worse than the overall model (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .05), yet after controlling for sample size, their performance was comparable with that of the overall model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>A single, overall model for m\u03bcSORS-based glucose monitoring achieves high accuracy across diverse patient subgroups, demonstrating its strong robustness. These findings support the clinical potential of depth-resolved Raman spectroscopy for non-invasive glucose monitoring.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial Registration:<\/jats:title>\n                    <jats:p>\n                      ClinicalTrials.gov NCT05921344;\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/clinicaltrials.gov\/study\/NCT05921344\">https:\/\/clinicaltrials.gov\/study\/NCT05921344<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19322968261464571","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T12:12:47Z","timestamp":1787055167000},"page":"1513-1524","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Non-Invasive Glucose Monitoring Using m\u03bcSORS: A Population-Based Validation Study Across Diverse Patient Variability"],"prefix":"10.1177","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5674-9043","authenticated-orcid":false,"given":"Xiaoru","family":"Feng","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing, China"},{"name":"School of Healthcare Management, Tsinghua Medicine, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China"},{"name":"Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai 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