Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning

A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. However, invasiveness, high cost, and limited lifecycle of continuous glucose monitoring (CGM) devices restrict their widespread use. To addre...

Full description

Saved in:

Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 30164 - 19
Main Authors:	Huang, Xinyu, Schmelter, Franziska, Seitzer, Christian, Martensen, Lars, Otzen, Hans, Piet, Artur, Witt, Oliver, Schröder, Torsten, Günther, Ulrich L., Marshall, Lisa, Grzegorzek, Marcin, Sina, Christian
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 18.08.2025 Nature Publishing Group Nature Portfolio
Subjects:	692/53/2423 692/700/478 Ablation Adult Algorithms Biomarkers - analysis Biomarkers - blood Blood Glucose - analysis Blood Glucose Self-Monitoring - instrumentation Blood Glucose Self-Monitoring - methods Blood levels Diabetes Diabetes mellitus Diet Engineered biomarker Feature selection Female Glucose Glucose - analysis Glucose - metabolism Glucose monitoring Healthy Volunteers Humanities and Social Sciences Humans Hypoglycemia Interstitial glucose prediction Invasiveness Learning algorithms Machine Learning Male Metabolism Middle Aged multidisciplinary Non-invasive CGM Nutrient deficiency Personalized nutrition Science Science (multidisciplinary) Sensors Wearable Electronic Devices Wearables Young Adult Personalized nutrition Wearables Engineered biomarker Non-invasive CGM Interstitial glucose prediction Machine learning
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. However, invasiveness, high cost, and limited lifecycle of continuous glucose monitoring (CGM) devices restrict their widespread use. To address these issues, we investigated machine learning (ML) approaches for glucose monitoring using data from non-invasive wearables. Our study comprised two phases involving healthy participants: The main study included two experimental sessions lasting 7–8 h with two standardized test meals, totaling over 1550 interstitial glucose (IG) measurements with CGM, and high-frequency multimodal data collected by two different non-invasive sensor devices. The follow-up study involved more than 14,400 IG measurements. Using ML approaches, correlations between glycemic measures and sensor data were assessed to estimate the feasibility of accurately predicting personalized IG alterations in real-time. An ensemble feature selection-based light gradient boosting machine (LightGBM) algorithm, omitting the need for food logs, was developed. This algorithm achieved a root mean squared error (RMSE) of 18.49 ± 0.1 mg/dL and a mean absolute percentage error (MAPE) of 15.58 ± 0.09%, demonstrating the feasibility of non-invasive glucose monitoring with high accuracy, which paves the way for novel approaches in the objective prevention of diet-related diseases.
AbstractList	A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. However, invasiveness, high cost, and limited lifecycle of continuous glucose monitoring (CGM) devices restrict their widespread use. To address these issues, we investigated machine learning (ML) approaches for glucose monitoring using data from non-invasive wearables. Our study comprised two phases involving healthy participants: The main study included two experimental sessions lasting 7–8 h with two standardized test meals, totaling over 1550 interstitial glucose (IG) measurements with CGM, and high-frequency multimodal data collected by two different non-invasive sensor devices. The follow-up study involved more than 14,400 IG measurements. Using ML approaches, correlations between glycemic measures and sensor data were assessed to estimate the feasibility of accurately predicting personalized IG alterations in real-time. An ensemble feature selection-based light gradient boosting machine (LightGBM) algorithm, omitting the need for food logs, was developed. This algorithm achieved a root mean squared error (RMSE) of 18.49 ± 0.1 mg/dL and a mean absolute percentage error (MAPE) of 15.58 ± 0.09%, demonstrating the feasibility of non-invasive glucose monitoring with high accuracy, which paves the way for novel approaches in the objective prevention of diet-related diseases. Abstract A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. However, invasiveness, high cost, and limited lifecycle of continuous glucose monitoring (CGM) devices restrict their widespread use. To address these issues, we investigated machine learning (ML) approaches for glucose monitoring using data from non-invasive wearables. Our study comprised two phases involving healthy participants: The main study included two experimental sessions lasting 7–8 h with two standardized test meals, totaling over 1550 interstitial glucose (IG) measurements with CGM, and high-frequency multimodal data collected by two different non-invasive sensor devices. The follow-up study involved more than 14,400 IG measurements. Using ML approaches, correlations between glycemic measures and sensor data were assessed to estimate the feasibility of accurately predicting personalized IG alterations in real-time. An ensemble feature selection-based light gradient boosting machine (LightGBM) algorithm, omitting the need for food logs, was developed. This algorithm achieved a root mean squared error (RMSE) of 18.49 ± 0.1 mg/dL and a mean absolute percentage error (MAPE) of 15.58 ± 0.09%, demonstrating the feasibility of non-invasive glucose monitoring with high accuracy, which paves the way for novel approaches in the objective prevention of diet-related diseases. A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. However, invasiveness, high cost, and limited lifecycle of continuous glucose monitoring (CGM) devices restrict their widespread use. To address these issues, we investigated machine learning (ML) approaches for glucose monitoring using data from non-invasive wearables. Our study comprised two phases involving healthy participants: The main study included two experimental sessions lasting 7–8 h with two standardized test meals, totaling over 1550 interstitial glucose (IG) measurements with CGM, and high-frequency multimodal data collected by two different non-invasive sensor devices. The follow-up study involved more than 14,400 IG measurements. Using ML approaches, correlations between glycemic measures and sensor data were assessed to estimate the feasibility of accurately predicting personalized IG alterations in real-time. An ensemble feature selection-based light gradient boosting machine (LightGBM) algorithm, omitting the need for food logs, was developed. This algorithm achieved a root mean squared error (RMSE) of 18.49 ± 0.1 mg/dL and a mean absolute percentage error (MAPE) of 15.58 ± 0.09%, demonstrating the feasibility of non-invasive glucose monitoring with high accuracy, which paves the way for novel approaches in the objective prevention of diet-related diseases. A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. However, invasiveness, high cost, and limited lifecycle of continuous glucose monitoring (CGM) devices restrict their widespread use. To address these issues, we investigated machine learning (ML) approaches for glucose monitoring using data from non-invasive wearables. Our study comprised two phases involving healthy participants: The main study included two experimental sessions lasting 7-8 h with two standardized test meals, totaling over 1550 interstitial glucose (IG) measurements with CGM, and high-frequency multimodal data collected by two different non-invasive sensor devices. The follow-up study involved more than 14,400 IG measurements. Using ML approaches, correlations between glycemic measures and sensor data were assessed to estimate the feasibility of accurately predicting personalized IG alterations in real-time. An ensemble feature selection-based light gradient boosting machine (LightGBM) algorithm, omitting the need for food logs, was developed. This algorithm achieved a root mean squared error (RMSE) of 18.49 ± 0.1 mg/dL and a mean absolute percentage error (MAPE) of 15.58 ± 0.09%, demonstrating the feasibility of non-invasive glucose monitoring with high accuracy, which paves the way for novel approaches in the objective prevention of diet-related diseases.A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. However, invasiveness, high cost, and limited lifecycle of continuous glucose monitoring (CGM) devices restrict their widespread use. To address these issues, we investigated machine learning (ML) approaches for glucose monitoring using data from non-invasive wearables. Our study comprised two phases involving healthy participants: The main study included two experimental sessions lasting 7-8 h with two standardized test meals, totaling over 1550 interstitial glucose (IG) measurements with CGM, and high-frequency multimodal data collected by two different non-invasive sensor devices. The follow-up study involved more than 14,400 IG measurements. Using ML approaches, correlations between glycemic measures and sensor data were assessed to estimate the feasibility of accurately predicting personalized IG alterations in real-time. An ensemble feature selection-based light gradient boosting machine (LightGBM) algorithm, omitting the need for food logs, was developed. This algorithm achieved a root mean squared error (RMSE) of 18.49 ± 0.1 mg/dL and a mean absolute percentage error (MAPE) of 15.58 ± 0.09%, demonstrating the feasibility of non-invasive glucose monitoring with high accuracy, which paves the way for novel approaches in the objective prevention of diet-related diseases.
ArticleNumber	30164
Author	Martensen, Lars Schröder, Torsten Piet, Artur Seitzer, Christian Otzen, Hans Günther, Ulrich L. Schmelter, Franziska Sina, Christian Witt, Oliver Marshall, Lisa Grzegorzek, Marcin Huang, Xinyu
Author_xml	– sequence: 1 givenname: Xinyu surname: Huang fullname: Huang, Xinyu email: hxy101726462@gmail.com organization: Institute of Medical Informatics, University of Luebeck – sequence: 2 givenname: Franziska surname: Schmelter fullname: Schmelter, Franziska organization: Institute of Nutritional Medicine, University of Luebeck and University Medical Center Schleswig-Holstein – sequence: 3 givenname: Christian surname: Seitzer fullname: Seitzer, Christian organization: Institute of Medical Informatics, University of Luebeck – sequence: 4 givenname: Lars surname: Martensen fullname: Martensen, Lars organization: Institute of Nutritional Medicine, University of Luebeck and University Medical Center Schleswig-Holstein, Perfood GmbH, Research and Development – sequence: 5 givenname: Hans surname: Otzen fullname: Otzen, Hans organization: Institute of Nutritional Medicine, University of Luebeck and University Medical Center Schleswig-Holstein – sequence: 6 givenname: Artur surname: Piet fullname: Piet, Artur organization: Institute of Medical Informatics, University of Luebeck – sequence: 7 givenname: Oliver surname: Witt fullname: Witt, Oliver organization: Perfood GmbH, Research and Development – sequence: 8 givenname: Torsten surname: Schröder fullname: Schröder, Torsten organization: Institute of Nutritional Medicine, University of Luebeck and University Medical Center Schleswig-Holstein, Perfood GmbH, Research and Development – sequence: 9 givenname: Ulrich L. surname: Günther fullname: Günther, Ulrich L. organization: Institute of Chemistry and Metabolomics, University of Luebeck – sequence: 10 givenname: Lisa surname: Marshall fullname: Marshall, Lisa organization: Institute of Experimental and Clinical Pharmacology and Toxicology, University of Luebeck, and University Medical Center Schleswig-Holstein, Center of Brain, Behavior and Metabolism (CBBM) – sequence: 11 givenname: Marcin surname: Grzegorzek fullname: Grzegorzek, Marcin organization: Institute of Medical Informatics, University of Luebeck, German Research Center for Artificial Intelligence (DFKI) – sequence: 12 givenname: Christian surname: Sina fullname: Sina, Christian organization: Institute of Nutritional Medicine, University of Luebeck and University Medical Center Schleswig-Holstein, Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE)
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/40825804$$D View this record in MEDLINE/PubMed
BookMark	eNp9kk1v1DAQhi1URMvSP8ABReLCJcWfSXxCqHxVqsQFzpbjjLNevPZiJ63aX493U_rBAV88mnnm9dh-X6KjEAMg9JrgM4JZ9z5zImRXYypqwklL69tn6IRiLmrKKD16FB-j05w3uCxBJSfyBTrmuKOiw_wEpU9udJP2Ve_iVqdfkHJlY6pcmEo4ucmV2uhnEzNUuwSDM5OLodSrNWg_rW9KOLgrN8za52rOLozVNeikew-50mGottqsXYDKl2wo5VfouS0snN7tK_Tzy-cf59_qy-9fL84_XtaGSz7VVDPNcYsHYaWRDBgmLaecguhbRii0YqCG99aaxgBjtjcEJO0oNmRgDSNshS4W3SHqjdolV-53o6J26pCIaVQ6Tc54ULZvONhBcIk73gnWW2m5NEVMQieL_Ap9WLR2c7-FwUCYkvZPRJ9WglurMV4pQllDRIOLwrs7hRR_z5AntXXZgPc6QJyzYpRjSWgr9oO__QfdxDmF8lYHSrRS8D315vFI97P8_dsC0AUwKeacwN4jBKu9h9TiIVU8pA4eUreliS1NucBhhPRw9n-6_gBoG8s_
Cites_doi	10.1007/s00109-004-0552-1 10.5194/gmd-7-1247-2014 10.1540/jsmr.40.169 10.1016/j.irbm.2023.100776 10.1038/s41598-017-13018-7 10.3390/nu14142927 10.1161/01.CIR.101.23.e215 10.1371/journal.pcbi.1010718 10.1186/s40104-017-0164-6 10.1530/JOE-16-0449 10.2340/00015555-1346 10.1109/ANTS63515.2024.10898929 10.1080/00207450600675027 10.2337/dc22-2290 10.1016/S0140-6736(14)60613-9 10.1186/s12916-022-02254-y 10.4239/wjd.v6.i13.1246 10.1177/1932296819855670 10.1109/ACCESS.2021.3079182 10.1016/j.jval.2022.07.020 10.2337/db09-1128 10.1093/bioinformatics/bty373 10.1109/ICAIIC60209.2024.10463200 10.1007/s12553-022-00690-7 10.1145/3715669.3726796 10.1111/dme.12184 10.1007/s00125-012-2525-1 10.1016/S2213-8587(18)30037-8 10.1038/s41746-020-0260-4 10.1145/2598394.2609856 10.2337/dc18-1843 10.1097/00000542-200612000-00011 10.1016/j.cell.2015.11.001 10.1111/j.1748-1716.1988.tb08353.x 10.1177/02537176211046525 10.1007/BF00403180 10.1016/j.imed.2024.05.002 10.3390/diagnostics3040385 10.1096/fasebj.31.1_supplement.lb797 10.1016/j.metabol.2023.155640 10.1093/ajcn/78.4.850S 10.3390/bios12030166 10.1038/s41746-021-00465-w 10.1109/PSGEC54663.2022.9880963 10.1371/journal.pone.0225613 10.1023/A:1010933404324 10.1016/j.arr.2017.10.003 10.1001/jamanetworkopen.2018.8102 10.21203/rs.2.20798/v1 10.1109/ICCSP48568.2020.9182434 10.2337/dc21-0162 10.1145/3220127.3220133 10.1109/BioCAS.2012.6418424 10.1007/978-1-4842-4470-8_18 10.1007/s11306-019-1532-3 10.1088/1757-899X/803/1/012012 10.1038/s41592-019-0686-2 10.1186/s43094-020-00151-w 10.1016/j.procir.2021.03.088 10.3390/e24050687 10.3390/ijerph192316080 10.1109/ICACCE.2018.8441729 10.1136/jim-2021-002278
ContentType	Journal Article
Copyright	The Author(s) 2025 2025. The Author(s). The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2025 2025
Copyright_xml	– notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2025 2025
DBID	C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI Q9U 7X8 5PM DOA
DOI	10.1038/s41598-025-14172-z
DatabaseName	Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials - QC Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection PML(ProQuest Medical Library) Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database (ProQuest) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	Publicly Available Content Database CrossRef MEDLINE - Academic MEDLINE
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Biology
EISSN	2045-2322
EndPage	19
ExternalDocumentID	oai_doaj_org_article_fb64efd549084853bf9f49c8209e89ce PMC12361560 40825804 10_1038_s41598_025_14172_z
Genre	Journal Article
GrantInformation_xml	– fundername: Universität zu Lübeck (3165)
GroupedDBID	0R~ 4.4 53G 5VS 7X7 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD AASML ABDBF ABUWG ACGFS ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M1P M2P M7P M~E NAO OK1 PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AAYXX AFFHD CITATION CGR CUY CVF ECM EIF NPM PUEGO 3V. 7XB 88A 8FK K9. M48 PKEHL PQEST PQUKI Q9U 7X8 5PM
ID	FETCH-LOGICAL-c494t-2a3a4070d5f9c93e30174242e5b7312e75d2c4bffc6ce33fbc1e92820c1d36313
IEDL.DBID	BENPR
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001553537500004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2045-2322
IngestDate	Tue Oct 14 18:49:47 EDT 2025 Tue Nov 04 02:04:36 EST 2025 Fri Sep 05 15:19:32 EDT 2025 Tue Oct 07 08:34:28 EDT 2025 Mon Sep 15 01:52:32 EDT 2025 Sat Nov 29 07:34:15 EST 2025 Tue Aug 19 01:10:39 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Personalized nutrition Wearables Engineered biomarker Non-invasive CGM Interstitial glucose prediction Machine learning
Language	English
License	2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c494t-2a3a4070d5f9c93e30174242e5b7312e75d2c4bffc6ce33fbc1e92820c1d36313
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
OpenAccessLink	https://www.proquest.com/docview/3240579541?pq-origsite=%requestingapplication%
PMID	40825804
PQID	3240579541
PQPubID	2041939
PageCount	19
ParticipantIDs	doaj_primary_oai_doaj_org_article_fb64efd549084853bf9f49c8209e89ce pubmedcentral_primary_oai_pubmedcentral_nih_gov_12361560 proquest_miscellaneous_3240912751 proquest_journals_3240579541 pubmed_primary_40825804 crossref_primary_10_1038_s41598_025_14172_z springer_journals_10_1038_s41598_025_14172_z
PublicationCentury	2000
PublicationDate	2025-08-18
PublicationDateYYYYMMDD	2025-08-18
PublicationDate_xml	– month: 08 year: 2025 text: 2025-08-18 day: 18
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: England
PublicationTitle	Scientific reports
PublicationTitleAbbrev	Sci Rep
PublicationTitleAlternate	Sci Rep
PublicationYear	2025
Publisher	Nature Publishing Group UK Nature Publishing Group Nature Portfolio
Publisher_xml	– name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio
References	S Nembrini (14172_CR25) 2018; 34 KCJ Yuen (14172_CR47) 2013; 30 R Lenhardt (14172_CR38) 2006; 105 AJ Lloyd (14172_CR23) 2019; 15 PRE Jarvis (14172_CR58) 2023; 146 BM Leon (14172_CR6) 2015; 6 T Chai (14172_CR32) 2014; 7 14172_CR63 P Virtanen (14172_CR71) 2020; 17 14172_CR62 L Heinemann (14172_CR49) 2019; 14 14172_CR65 K Prakash (14172_CR46) 2023; 132 JC Goldsack (14172_CR59) 2020; 3 C Andrade (14172_CR69) 2021; 43 14172_CR29 Z Geng (14172_CR50) 2017; 7 NM Templeman (14172_CR2) 2017; 232 SK Vashist (14172_CR48) 2013; 3 MO Bekkink (14172_CR19) 2019; 42 A Aloraynan (14172_CR54) 2022; 12 H Hoon Kwon (14172_CR9) 2012; 92 AI Adler (14172_CR26) 2022; 24 L Breiman (14172_CR28) 2001; 45 O Ben-Yacov (14172_CR16) 2021; 44 14172_CR60 14172_CR52 M Shokrekhodaei (14172_CR57) 2021; 9 14172_CR56 Y Hagiwara (14172_CR68) 2023; 26 14172_CR55 A Ramón (14172_CR67) 2022; 70 14172_CR15 AL Goldberger (14172_CR36) 2000; 101 WJ van den Brink (14172_CR34) 2022; 14 SH Ley (14172_CR11) 2014; 383 D Zeevi (14172_CR14) 2015; 163 D Chiccoid (14172_CR27) 2022 M Mayo (14172_CR61) 2019; 14 B Bent (14172_CR22) 2021 RM Ortega (14172_CR24) 2015; 31 A Sharma (14172_CR8) 2020; 6 F Prattichizzo (14172_CR3) 2018; 41 H Mendes-Soares (14172_CR12) 2019; 2 H Agrawal (14172_CR53) 2022; 12 WHM Saris (14172_CR4) 2003; 78 P Cho (14172_CR37) 2023; 101 J Oberle (14172_CR43) 1988; 132 DD Wang (14172_CR13) 2018; 6 CJ Popp (14172_CR18) 2023; 5 E Ferrannini (14172_CR1) 1991; 34 L Hu (14172_CR66) 2022; 19 M Rein (14172_CR17) 2022; 20 M Maritsch (14172_CR20) 2020; 152 SG Rosen (14172_CR44) 1983; 245 H Cheng (14172_CR31) 2017; 8 M Ristow (14172_CR7) 2004; 82 M Benchekroun (14172_CR64) 2023; 44 N Misu (14172_CR41) 2004; 40 V Lehmann (14172_CR21) 2023; 46 JA Johnson (14172_CR5) 2012; 55 B Lindemann (14172_CR30) 2021; 99 X Huang (14172_CR51) 2024; 4 E Bisong (14172_CR70) 2019 14172_CR72 14172_CR35 N Dolu (14172_CR45) 2006; 116 14172_CR33 M Sanchez-Alavez (14172_CR42) 2009; 59 14172_CR39 VV Lelleck (14172_CR10) 2022; 14 TC Barbosa (14172_CR40) 2017; 31
References_xml	– volume: 82 start-page: 510 year: 2004 ident: 14172_CR7 publication-title: J. Mol. Med. doi: 10.1007/s00109-004-0552-1 – volume: 7 start-page: 1247 year: 2014 ident: 14172_CR32 publication-title: Geosci. Model Dev. doi: 10.5194/gmd-7-1247-2014 – volume: 40 start-page: 169 issue: 4–5 year: 2004 ident: 14172_CR41 publication-title: J. Smooth Muscle Res Nihon Heikatsukin Gakkai Kikanshi doi: 10.1540/jsmr.40.169 – volume: 44 start-page: 100776 year: 2023 ident: 14172_CR64 publication-title: IRBM doi: 10.1016/j.irbm.2023.100776 – volume: 7 start-page: 12650 year: 2017 ident: 14172_CR50 publication-title: Sci. Rep. doi: 10.1038/s41598-017-13018-7 – volume: 14 start-page: 2927 year: 2022 ident: 14172_CR10 publication-title: Nutrients doi: 10.3390/nu14142927 – volume: 101 start-page: e215 year: 2000 ident: 14172_CR36 publication-title: Circulation doi: 10.1161/01.CIR.101.23.e215 – year: 2022 ident: 14172_CR27 publication-title: PLOS Comput. Biol. doi: 10.1371/journal.pcbi.1010718 – volume: 8 start-page: 38 year: 2017 ident: 14172_CR31 publication-title: J. Anim. Sci. Biotechnol. doi: 10.1186/s40104-017-0164-6 – volume: 232 start-page: 173 year: 2017 ident: 14172_CR2 publication-title: J. Endocrinol. doi: 10.1530/JOE-16-0449 – volume: 92 start-page: 241 year: 2012 ident: 14172_CR9 publication-title: Acta Derm. Venereol. doi: 10.2340/00015555-1346 – ident: 14172_CR62 doi: 10.1109/ANTS63515.2024.10898929 – volume: 14 start-page: 4465 year: 2022 ident: 14172_CR34 publication-title: Wearable Technol. Nutr. – volume: 116 start-page: 715 year: 2006 ident: 14172_CR45 publication-title: Int. J. Neurosci. doi: 10.1080/00207450600675027 – volume: 46 start-page: 1 year: 2023 ident: 14172_CR21 publication-title: Diabetes Care doi: 10.2337/dc22-2290 – volume: 383 start-page: 1999 year: 2014 ident: 14172_CR11 publication-title: Lancet doi: 10.1016/S0140-6736(14)60613-9 – volume: 20 start-page: 56 year: 2022 ident: 14172_CR17 publication-title: BMC Med. doi: 10.1186/s12916-022-02254-y – volume: 6 start-page: 1246 year: 2015 ident: 14172_CR6 publication-title: World J. Diabetes doi: 10.4239/wjd.v6.i13.1246 – volume: 14 start-page: 135 year: 2019 ident: 14172_CR49 publication-title: J. Diabetes Sci. Technol. doi: 10.1177/1932296819855670 – volume: 9 start-page: 73029 year: 2021 ident: 14172_CR57 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3079182 – volume: 26 start-page: 269 year: 2023 ident: 14172_CR68 publication-title: Value Health doi: 10.1016/j.jval.2022.07.020 – volume: 59 start-page: 43 year: 2009 ident: 14172_CR42 publication-title: Diabetes doi: 10.2337/db09-1128 – volume: 34 start-page: 3711 year: 2018 ident: 14172_CR25 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty373 – ident: 14172_CR56 doi: 10.1109/ICAIIC60209.2024.10463200 – volume: 12 start-page: 955 year: 2022 ident: 14172_CR53 publication-title: Health Technol. doi: 10.1007/s12553-022-00690-7 – ident: 14172_CR39 doi: 10.1145/3715669.3726796 – volume: 30 start-page: 651 year: 2013 ident: 14172_CR47 publication-title: Diabet. Med. doi: 10.1111/dme.12184 – volume: 55 start-page: 1607 year: 2012 ident: 14172_CR5 publication-title: Diabetologia doi: 10.1007/s00125-012-2525-1 – volume: 6 start-page: 416 year: 2018 ident: 14172_CR13 publication-title: Lancet Diabetes Endocrinol. doi: 10.1016/S2213-8587(18)30037-8 – volume: 3 start-page: 55 year: 2020 ident: 14172_CR59 publication-title: Npj Digit. Med. doi: 10.1038/s41746-020-0260-4 – ident: 14172_CR33 doi: 10.1145/2598394.2609856 – volume: 42 start-page: 689 year: 2019 ident: 14172_CR19 publication-title: Diabetes Care doi: 10.2337/dc18-1843 – volume: 105 start-page: 1117 year: 2006 ident: 14172_CR38 publication-title: Anesthesiology doi: 10.1097/00000542-200612000-00011 – volume: 163 start-page: 1079 year: 2015 ident: 14172_CR14 publication-title: Cell doi: 10.1016/j.cell.2015.11.001 – volume: 132 start-page: 459 year: 1988 ident: 14172_CR43 publication-title: Acta Physiol. Scand. doi: 10.1111/j.1748-1716.1988.tb08353.x – volume: 245 start-page: E616 issue: 6 year: 1983 ident: 14172_CR44 publication-title: Am. J. Physiol. – volume: 43 start-page: 555 year: 2021 ident: 14172_CR69 publication-title: Indian J. Psychol. Med. doi: 10.1177/02537176211046525 – volume: 34 start-page: 416 year: 1991 ident: 14172_CR1 publication-title: Diabetologia doi: 10.1007/BF00403180 – volume: 4 start-page: 226 year: 2024 ident: 14172_CR51 publication-title: Intell. Med. doi: 10.1016/j.imed.2024.05.002 – volume: 3 start-page: 385 year: 2013 ident: 14172_CR48 publication-title: Diagnostics doi: 10.3390/diagnostics3040385 – ident: 14172_CR29 – volume: 152 start-page: 1 year: 2020 ident: 14172_CR20 publication-title: Ext. Abstr. 2020 CHI Conf. Hum. Factors Comput. Syst. – volume: 31 start-page: lb797 year: 2017 ident: 14172_CR40 publication-title: FASEB J. doi: 10.1096/fasebj.31.1_supplement.lb797 – volume: 146 start-page: 155640 year: 2023 ident: 14172_CR58 publication-title: Metab. Clin. Exp. doi: 10.1016/j.metabol.2023.155640 – volume: 78 start-page: 850 year: 2003 ident: 14172_CR4 publication-title: Am. J. Clin. Nutr. doi: 10.1093/ajcn/78.4.850S – volume: 12 start-page: 166 year: 2022 ident: 14172_CR54 publication-title: Biosensors doi: 10.3390/bios12030166 – year: 2021 ident: 14172_CR22 publication-title: Npj Digit. Med. doi: 10.1038/s41746-021-00465-w – ident: 14172_CR35 doi: 10.1109/PSGEC54663.2022.9880963 – volume: 14 start-page: e0225613 year: 2019 ident: 14172_CR61 publication-title: PLoS ONE doi: 10.1371/journal.pone.0225613 – volume: 45 start-page: 5 year: 2001 ident: 14172_CR28 publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 41 start-page: 1 year: 2018 ident: 14172_CR3 publication-title: Ageing Res. Rev. doi: 10.1016/j.arr.2017.10.003 – ident: 14172_CR72 – volume: 2 start-page: e188102 year: 2019 ident: 14172_CR12 publication-title: JAMA Netw. Open doi: 10.1001/jamanetworkopen.2018.8102 – ident: 14172_CR15 doi: 10.21203/rs.2.20798/v1 – ident: 14172_CR55 doi: 10.1109/ICCSP48568.2020.9182434 – volume: 44 start-page: 1980 year: 2021 ident: 14172_CR16 publication-title: Diabetes Care doi: 10.2337/dc21-0162 – volume: 5 start-page: 1 year: 2023 ident: 14172_CR18 publication-title: JAMA Netw. Open – ident: 14172_CR60 doi: 10.1145/3220127.3220133 – ident: 14172_CR63 doi: 10.1109/BioCAS.2012.6418424 – start-page: 215 volume-title: Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners year: 2019 ident: 14172_CR70 doi: 10.1007/978-1-4842-4470-8_18 – volume: 15 start-page: 1 year: 2019 ident: 14172_CR23 publication-title: Metabolomics doi: 10.1007/s11306-019-1532-3 – ident: 14172_CR65 doi: 10.1088/1757-899X/803/1/012012 – volume: 17 start-page: 261 year: 2020 ident: 14172_CR71 publication-title: Nat. Methods doi: 10.1038/s41592-019-0686-2 – volume: 6 start-page: 1 year: 2020 ident: 14172_CR8 publication-title: Futur. J. Pharm. Sci. doi: 10.1186/s43094-020-00151-w – volume: 99 start-page: 650 year: 2021 ident: 14172_CR30 publication-title: Proc. CIRP doi: 10.1016/j.procir.2021.03.088 – volume: 132 start-page: 444 year: 2023 ident: 14172_CR46 publication-title: Exp. Clin. Endocrinol. Diabetes – volume: 31 start-page: 38 year: 2015 ident: 14172_CR24 publication-title: Nutr. Hosp. – volume: 24 start-page: 687 year: 2022 ident: 14172_CR26 publication-title: Entropy doi: 10.3390/e24050687 – volume: 19 start-page: 16080 year: 2022 ident: 14172_CR66 publication-title: Int. J. Environ. Res. Publ. Health doi: 10.3390/ijerph192316080 – ident: 14172_CR52 doi: 10.1109/ICACCE.2018.8441729 – volume: 70 start-page: 1472 year: 2022 ident: 14172_CR67 publication-title: J. Investig. Med. doi: 10.1136/jim-2021-002278 – volume: 101 start-page: e215 year: 2023 ident: 14172_CR37 publication-title: PhysioNet
SSID	ssj0000529419
Score	2.456928
Snippet	A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals.... Abstract A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in...
SourceID	doaj pubmedcentral proquest pubmed crossref springer
SourceType	Open Website Open Access Repository Aggregation Database Index Database Publisher
StartPage	30164
SubjectTerms	692/53/2423 692/700/478 Ablation Adult Algorithms Biomarkers - analysis Biomarkers - blood Blood Glucose - analysis Blood Glucose Self-Monitoring - instrumentation Blood Glucose Self-Monitoring - methods Blood levels Diabetes Diabetes mellitus Diet Engineered biomarker Feature selection Female Glucose Glucose - analysis Glucose - metabolism Glucose monitoring Healthy Volunteers Humanities and Social Sciences Humans Hypoglycemia Interstitial glucose prediction Invasiveness Learning algorithms Machine Learning Male Metabolism Middle Aged multidisciplinary Non-invasive CGM Nutrient deficiency Personalized nutrition Science Science (multidisciplinary) Sensors Wearable Electronic Devices Wearables Young Adult
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LbxQxDI5QVSQuiDcDLQoSN4g6ec0kR1qoOKCKA6Deopk8lj0wrWa2oPbXYyezS5eHuHDd5ODNZ49t2f5MyAstAOQ-WGaNaZiCCJeZYDWLdeQqyVaEPMf9-X17cmJOT-2Ha6u-sCes0AOXhztIfaNiChoLVAp8S59sUtaD47LRWB_x6wtRz7VkqrB6C6u4nadkamkOJvBUOE0mNOMKvDa72vJEmbD_T1Hm782Sv1RMsyM6vkNuzxEkfV0kv0tuxOEeuVl2Sl7eJ-Ob5QIXgVAcrMfem3GiEJhS5IUYsTEAFI7Ojer0fMQ6DWID57TMRF7S5WZIa6LYF7-g38EecMZqot0Q6NfcgBnpvHFi8YB8On778egdmxcrMK-sWjHRyQ4SuTroZL2VEYwcMmQlou5byUVsdRBe9Sn5xkcpU-95tJCb1Z4H2UguH5Kd4WyIjwlNfRJW9yFFiKx8r4zVHW90m1rtQydVRV6uH9mdF_4Ml-ve0rgCiQNIXIbEXVXkEHHY3ETu6_wDaISbNcL9SyMqsrdG0c0GOTnkHdSt1YpX5PnmGEwJ6yPdEM8uyh2LhPdw51EBfSMJ7uXWpob_Y7bUYUvU7ZNh-SXTdWd-GwgsK_JqrTk_5fr7Wzz5H2_xlNwSqPLI4Wv2yM5qvIj7ZNd_Wy2n8Vm2mR9fYxxN priority: 102 providerName: Directory of Open Access Journals
Title	Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning
URI	https://link.springer.com/article/10.1038/s41598-025-14172-z https://www.ncbi.nlm.nih.gov/pubmed/40825804 https://www.proquest.com/docview/3240579541 https://www.proquest.com/docview/3240912751 https://pubmed.ncbi.nlm.nih.gov/PMC12361560 https://doaj.org/article/fb64efd549084853bf9f49c8209e89ce
Volume	15
WOSCitedRecordID	wos001553537500004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest - Health & Medical Complete保健、医学与药学数据库 customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwELbYFiQuvBcCS2UkbhBtEtuJfUIs7AoktqoQoHKKEj9KD6Ql6YJ2fz0zjptVeV24-BD7YOebsceemW8IeSoyALk2KlZS5jEHCzeWRonYJjbljhWZ8Xncn94V06mcz9UsPLh1Iaxyuyf6jdqsNL6RHyJxnCiU4OmL9bcYq0ahdzWU0NgjY2Qq4yMyPjqezt4Pryzox-KpCtkyCZOHHZxYmFWWiTjlcHrHFzsnkifu_5O1-XvQ5C-eU38gndz836XcIjeCKUpf9rJzm1yxzR1yrS9OeX6XtK-XC6woQjFDH4N42o6ChUuRYKLFCAOQXBoi3um6RYcPggz9tE-uPKfLIduroxhgv6A_QLEwWaujVWPoVx_JaWkoXbG4Rz6eHH949SYOFRpizRXfxFnFKrgRJkY4pRWzsFvAVZtnVtQFSzNbCJNpXjunc20Zc7VOrYJLXqJTw3KWsn0yalaNfUCoq12mRG2cBRNN11wqUaW5KFwhtKkYj8izLUrluifiKL0Dncmyx7QETEuPaXkRkSMEchiJJNr-w6pdlEEnS1fn3Doj0PfJwWypnXJcaZieslJpG5GDLX5l0OyuvAQvIk-GbtBJdLRUjV2d9WMUMufDmPu91AwzwQLfQiawHrkjTztT3e1pll8877cnygELNSLPt6J3Oa-__4uH_17GI3I9Q21Aml95QEab9sw-Jlf1982yaydkr5gXvpWToFwT_24B7Wk2w7aAdjx7ezr7_BMruDGo
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1JbxMxFLZKAcGFfQkUMBKcYNSMl4x9QAgoVauGiENBubkzXkIOTMJMSpX-KH4j73lmUoXt1gPXsTXy8r3nZ7_lI-SZZLDJhdOJVmqQCLBwE-W0THzfpyLwjLmYx_15mI1GajzWHzfIjy4XBsMqO50YFbWbWXwj38bCcTLTUqSv598SZI1C72pHodHA4sAvT-DKVr_a34H9fc7Y7vvDd3tJyyqQWKHFImE5z-EW03cyaKu5B4TD9VAwL4uMp8xn0jErihDswHrOQ2FTr-Fi0rep4wOecvjvBXIR9HiGIWTZOFu96aDXTKS6zc3pc7Vdw_mIOWxMJqkAWyE5XTv_Ik3An2zb30M0f_HTxuNv9_r_tnA3yLXW0KZvGsm4STZ8eYtcbqg3l7dJtTOdIF8KxfoDGKJU1RTsd4rlMyqMnwC5pG08P51X6M5CCEM7bVJHl3S6ymWrKaYPTOgJTBNT0Wqal45-jXGqnrbEHJM75NO5TPgu2Sxnpb9PaCgC07JwwYMBaguhtMzTgcxCJq3LueiRFx0qzLwpM2JieABXpsGQAQyZiCFz2iNvETirnlgiPH6YVRPTahwTioHwwUn07Aowyoqgg9AWhqe90tb3yFaHF9PqrdqcgaVHnq6aQeOgGykv_ey46aORFwD63GtQuhoJ0pdL1Yf5qDX8rg11vaWcfolVzWMZILC_e-RlB_Wzcf19LR78expPyJW9ww9DM9wfHTwkVxlKIhY0Vltkc1Ed-0fkkv2-mNbV4yjKlBydtwj8BFMxhcE
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jj9MwFLaGYREX9qUwgJHgBFEbL419QAgoI0YzVD0AmptJvJQeSEvSYdT5afw63nOSjsp2mwPX2Iq8fO_52W_5CHkiGWxy4XSilRomAizcRDktEz_wqQg8Yy7mcX86yMZjdXioJ1vkR5cLg2GVnU6MitrNLb6R97FwnMy0FGk_tGERk9Huy8W3BBmk0NPa0Wk0ENn3q2O4vtUv9kaw108Z23374c27pGUYSKzQYpmwnOdwoxk4GbTV3APa4aoomJdFxlPmM-mYFUUIdmg956GwqddwSRnY1PEhTzn89xw5nwkpUbres8n6fQc9aCLVbZ7OgKt-DWcl5rMxmaQC7IbkZOMsjJQBf7Jzfw_X_MVnG4_C3av_8yJeI1daA5y-aiTmOtny5Q1ysaHkXN0k1Wg2RR4VinUJMHSpqinY9RTLalQYVwHySts4f7qo0M2F0IZ22qSUruhsneNWU0wrmNJjmCamqNU0Lx39GuNXPW0JO6a3yMczmfBtsl3OS3-X0FAEpmXhggfD1BZCaZmnQ5mFTFqXc9EjzzqEmEVTfsTEsAGuTIMnA3gyEU_mpEdeI4jWPbF0ePwwr6am1UQmFEPhg5Po8RVgrBVBB6EtDE97pa3vkZ0OO6bVZ7U5BU6PPF43gyZC91Je-vlR00cjXwD0udMgdj0SpDWXagDzURtY3hjqZks5-xKrncfyQGCX98jzDvan4_r7Wtz79zQekUuAfHOwN96_Ty4zFEqsc6x2yPayOvIPyAX7fTmrq4dRqin5fNYS8BODLI6O
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Digital+biomarkers+for+interstitial+glucose+prediction+in+healthy+individuals+using+wearables+and+machine+learning&rft.jtitle=Scientific+reports&rft.au=Huang%2C+Xinyu&rft.au=Schmelter%2C+Franziska&rft.au=Seitzer%2C+Christian&rft.au=Martensen%2C+Lars&rft.date=2025-08-18&rft.pub=Nature+Publishing+Group&rft.eissn=2045-2322&rft.volume=15&rft.issue=1&rft.spage=30164&rft_id=info:doi/10.1038%2Fs41598-025-14172-z&rft.externalDBID=HAS_PDF_LINK
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon