Deep Phenotyping of Obesity: Electronic Health Record–Based Temporal Modeling Study
Obesity affects approximately 40% of adults and 15%-20% of children and adolescents in the United States, and poses significant economic and psychosocial burdens. Currently, patient responses to any single antiobesity medication (AOM) vary significantly, making obesity deep phenotyping and associate...
Uloženo v:
| Vydáno v: | Journal of medical Internet research Ročník 27; číslo 25; s. e70140 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Canada
Journal of Medical Internet Research
20.08.2025
JMIR Publications |
| Témata: | |
| ISSN: | 1438-8871, 1439-4456, 1438-8871 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Obesity affects approximately 40% of adults and 15%-20% of children and adolescents in the United States, and poses significant economic and psychosocial burdens. Currently, patient responses to any single antiobesity medication (AOM) vary significantly, making obesity deep phenotyping and associated precision medicine important targets of investigation.
This study aimed to evaluate the potential of electronic health records (EHR) as a primary data source for obesity deep phenotyping. We conducted an in-depth analysis of the data elements and quality available from obesity patients prior to pharmacotherapy and applied a multimodal longitudinal deep autoencoder to investigate the feasibility, data requirements, clustering patterns, and challenges associated with EHR-based obesity deep phenotyping.
We analyzed 53,688 pre-AOM periods from 32,969 patients with obesity or overweight who underwent medium- to long-term AOM treatment. A total of 92 laboratory and vital measurements, along with 79 ICD (International Classification of Diseases)-derived clinical classifications software (CCS) codes recorded within one year prior to AOM treatment, were used to train a gated recurrent unit with decay-based longitudinal autoencoder (GRU-D-AE) to generate dense embeddings for each pre-AOM record. Principal component analysis and Gaussian mixture modeling (GMM) were applied to identify clusters.
Our analysis identified at least 9 clusters, with 5 exhibiting distinct and explainable clinical relevance. Certain clusters show characteristics overlapping with phenotypes from traditional phenotyping strategy. Results from multiple training folds demonstrated stable clustering patterns in 2D space and reproducible clinical significance. However, challenges persist regarding the stability of missing data imputation across folds, maintaining consistency in input features, and effectively visualizing complex diseases in low-dimensional spaces.
In this proof-of-concept study, we demonstrated longitudinal EHR as a valuable resource for deep phenotyping the pre-AOM period at per patient visit level. Our analysis revealed the presence of clusters with distinct clinical significance, which could have implications in AOM treatment options. Further research using larger, independent cohorts is necessary to validate the reproducibility and clinical relevance of these clusters, uncover more detailed substructures and corresponding AOM treatment responses. |
|---|---|
| AbstractList | Obesity affects approximately 40% of adults and 15%-20% of children and adolescents in the United States, and poses significant economic and psychosocial burdens. Currently, patient responses to any single antiobesity medication (AOM) vary significantly, making obesity deep phenotyping and associated precision medicine important targets of investigation.BackgroundObesity affects approximately 40% of adults and 15%-20% of children and adolescents in the United States, and poses significant economic and psychosocial burdens. Currently, patient responses to any single antiobesity medication (AOM) vary significantly, making obesity deep phenotyping and associated precision medicine important targets of investigation.This study aimed to evaluate the potential of electronic health records (EHR) as a primary data source for obesity deep phenotyping. We conducted an in-depth analysis of the data elements and quality available from obesity patients prior to pharmacotherapy and applied a multimodal longitudinal deep autoencoder to investigate the feasibility, data requirements, clustering patterns, and challenges associated with EHR-based obesity deep phenotyping.ObjectiveThis study aimed to evaluate the potential of electronic health records (EHR) as a primary data source for obesity deep phenotyping. We conducted an in-depth analysis of the data elements and quality available from obesity patients prior to pharmacotherapy and applied a multimodal longitudinal deep autoencoder to investigate the feasibility, data requirements, clustering patterns, and challenges associated with EHR-based obesity deep phenotyping.We analyzed 53,688 pre-AOM periods from 32,969 patients with obesity or overweight who underwent medium- to long-term AOM treatment. A total of 92 laboratory and vital measurements, along with 79 ICD (International Classification of Diseases)-derived clinical classifications software (CCS) codes recorded within one year prior to AOM treatment, were used to train a gated recurrent unit with decay-based longitudinal autoencoder (GRU-D-AE) to generate dense embeddings for each pre-AOM record. Principal component analysis and Gaussian mixture modeling (GMM) were applied to identify clusters.MethodsWe analyzed 53,688 pre-AOM periods from 32,969 patients with obesity or overweight who underwent medium- to long-term AOM treatment. A total of 92 laboratory and vital measurements, along with 79 ICD (International Classification of Diseases)-derived clinical classifications software (CCS) codes recorded within one year prior to AOM treatment, were used to train a gated recurrent unit with decay-based longitudinal autoencoder (GRU-D-AE) to generate dense embeddings for each pre-AOM record. Principal component analysis and Gaussian mixture modeling (GMM) were applied to identify clusters.Our analysis identified at least 9 clusters, with 5 exhibiting distinct and explainable clinical relevance. Certain clusters show characteristics overlapping with phenotypes from traditional phenotyping strategy. Results from multiple training folds demonstrated stable clustering patterns in 2D space and reproducible clinical significance. However, challenges persist regarding the stability of missing data imputation across folds, maintaining consistency in input features, and effectively visualizing complex diseases in low-dimensional spaces.ResultsOur analysis identified at least 9 clusters, with 5 exhibiting distinct and explainable clinical relevance. Certain clusters show characteristics overlapping with phenotypes from traditional phenotyping strategy. Results from multiple training folds demonstrated stable clustering patterns in 2D space and reproducible clinical significance. However, challenges persist regarding the stability of missing data imputation across folds, maintaining consistency in input features, and effectively visualizing complex diseases in low-dimensional spaces.In this proof-of-concept study, we demonstrated longitudinal EHR as a valuable resource for deep phenotyping the pre-AOM period at per patient visit level. Our analysis revealed the presence of clusters with distinct clinical significance, which could have implications in AOM treatment options. Further research using larger, independent cohorts is necessary to validate the reproducibility and clinical relevance of these clusters, uncover more detailed substructures and corresponding AOM treatment responses.ConclusionsIn this proof-of-concept study, we demonstrated longitudinal EHR as a valuable resource for deep phenotyping the pre-AOM period at per patient visit level. Our analysis revealed the presence of clusters with distinct clinical significance, which could have implications in AOM treatment options. Further research using larger, independent cohorts is necessary to validate the reproducibility and clinical relevance of these clusters, uncover more detailed substructures and corresponding AOM treatment responses. Abstract BackgroundObesity affects approximately 40% of adults and 15%‐20% of children and adolescents in the United States, and poses significant economic and psychosocial burdens. Currently, patient responses to any single antiobesity medication (AOM) vary significantly, making obesity deep phenotyping and associated precision medicine important targets of investigation. ObjectiveThis study aimed to evaluate the potential of electronic health records (EHR) as a primary data source for obesity deep phenotyping. We conducted an in-depth analysis of the data elements and quality available from obesity patients prior to pharmacotherapy and applied a multimodal longitudinal deep autoencoder to investigate the feasibility, data requirements, clustering patterns, and challenges associated with EHR-based obesity deep phenotyping. MethodsWe analyzed 53,688 pre-AOM periods from 32,969 patients with obesity or overweight who underwent medium- to long-term AOM treatment. A total of 92 laboratory and vital measurements, along with 79 ICDInternational Classification of Diseases ResultsOur analysis identified at least 9 clusters, with 5 exhibiting distinct and explainable clinical relevance. Certain clusters show characteristics overlapping with phenotypes from traditional phenotyping strategy. Results from multiple training folds demonstrated stable clustering patterns in 2D space and reproducible clinical significance. However, challenges persist regarding the stability of missing data imputation across folds, maintaining consistency in input features, and effectively visualizing complex diseases in low-dimensional spaces. ConclusionsIn this proof-of-concept study, we demonstrated longitudinal EHR as a valuable resource for deep phenotyping the pre-AOM period at per patient visit level. Our analysis revealed the presence of clusters with distinct clinical significance, which could have implications in AOM treatment options. Further research using larger, independent cohorts is necessary to validate the reproducibility and clinical relevance of these clusters, uncover more detailed substructures and corresponding AOM treatment responses. Obesity affects approximately 40% of adults and 15%‐20% of children and adolescents in the United States, and poses significant economic and psychosocial burdens. Currently, patient responses to any single antiobesity medication (AOM) vary significantly, making obesity deep phenotyping and associated precision medicine important targets of investigation. This study aimed to evaluate the potential of electronic health records (EHR) as a primary data source for obesity deep phenotyping. We conducted an in-depth analysis of the data elements and quality available from obesity patients prior to pharmacotherapy and applied a multimodal longitudinal deep autoencoder to investigate the feasibility, data requirements, clustering patterns, and challenges associated with EHR-based obesity deep phenotyping. We analyzed 53,688 pre-AOM periods from 32,969 patients with obesity or overweight who underwent medium- to long-term AOM treatment. A total of 92 laboratory and vital measurements, along with 79 ICD (International Classification of Diseases)-derived clinical classifications software (CCS) codes recorded within one year prior to AOM treatment, were used to train a gated recurrent unit with decay-based longitudinal autoencoder (GRU-D-AE) to generate dense embeddings for each pre-AOM record. Principal component analysis and Gaussian mixture modeling (GMM) were applied to identify clusters. Our analysis identified at least 9 clusters, with 5 exhibiting distinct and explainable clinical relevance. Certain clusters show characteristics overlapping with phenotypes from traditional phenotyping strategy. Results from multiple training folds demonstrated stable clustering patterns in 2D space and reproducible clinical significance. However, challenges persist regarding the stability of missing data imputation across folds, maintaining consistency in input features, and effectively visualizing complex diseases in low-dimensional spaces. In this proof-of-concept study, we demonstrated longitudinal EHR as a valuable resource for deep phenotyping the pre-AOM period at per patient visit level. Our analysis revealed the presence of clusters with distinct clinical significance, which could have implications in AOM treatment options. Further research using larger, independent cohorts is necessary to validate the reproducibility and clinical relevance of these clusters, uncover more detailed substructures and corresponding AOM treatment responses. Background Obesity affects approximately 40% of adults and 15%‐20% of children and adolescents in the United States, and poses significant economic and psychosocial burdens. Currently, patient responses to any single antiobesity medication (AOM) vary significantly, making obesity deep phenotyping and associated precision medicine important targets of investigation. Objective This study aimed to evaluate the potential of electronic health records (EHR) as a primary data source for obesity deep phenotyping. We conducted an in-depth analysis of the data elements and quality available from obesity patients prior to pharmacotherapy and applied a multimodal longitudinal deep autoencoder to investigate the feasibility, data requirements, clustering patterns, and challenges associated with EHR-based obesity deep phenotyping. Methods We analyzed 53,688 pre-AOM periods from 32,969 patients with obesity or overweight who underwent medium- to long-term AOM treatment. A total of 92 laboratory and vital measurements, along with 79 ICD (International Classification of Diseases)-derived clinical classifications software (CCS) codes recorded within one year prior to AOM treatment, were used to train a gated recurrent unit with decay-based longitudinal autoencoder (GRU-D-AE) to generate dense embeddings for each pre-AOM record. Principal component analysis and Gaussian mixture modeling (GMM) were applied to identify clusters. Results Our analysis identified at least 9 clusters, with 5 exhibiting distinct and explainable clinical relevance. Certain clusters show characteristics overlapping with phenotypes from traditional phenotyping strategy. Results from multiple training folds demonstrated stable clustering patterns in 2D space and reproducible clinical significance. However, challenges persist regarding the stability of missing data imputation across folds, maintaining consistency in input features, and effectively visualizing complex diseases in low-dimensional spaces. Conclusions In this proof-of-concept study, we demonstrated longitudinal EHR as a valuable resource for deep phenotyping the pre-AOM period at per patient visit level. Our analysis revealed the presence of clusters with distinct clinical significance, which could have implications in AOM treatment options. Further research using larger, independent cohorts is necessary to validate the reproducibility and clinical relevance of these clusters, uncover more detailed substructures and corresponding AOM treatment responses. Obesity affects approximately 40% of adults and 15%-20% of children and adolescents in the United States, and poses significant economic and psychosocial burdens. Currently, patient responses to any single antiobesity medication (AOM) vary significantly, making obesity deep phenotyping and associated precision medicine important targets of investigation. This study aimed to evaluate the potential of electronic health records (EHR) as a primary data source for obesity deep phenotyping. We conducted an in-depth analysis of the data elements and quality available from obesity patients prior to pharmacotherapy and applied a multimodal longitudinal deep autoencoder to investigate the feasibility, data requirements, clustering patterns, and challenges associated with EHR-based obesity deep phenotyping. We analyzed 53,688 pre-AOM periods from 32,969 patients with obesity or overweight who underwent medium- to long-term AOM treatment. A total of 92 laboratory and vital measurements, along with 79 ICD (International Classification of Diseases)-derived clinical classifications software (CCS) codes recorded within one year prior to AOM treatment, were used to train a gated recurrent unit with decay-based longitudinal autoencoder (GRU-D-AE) to generate dense embeddings for each pre-AOM record. Principal component analysis and Gaussian mixture modeling (GMM) were applied to identify clusters. Our analysis identified at least 9 clusters, with 5 exhibiting distinct and explainable clinical relevance. Certain clusters show characteristics overlapping with phenotypes from traditional phenotyping strategy. Results from multiple training folds demonstrated stable clustering patterns in 2D space and reproducible clinical significance. However, challenges persist regarding the stability of missing data imputation across folds, maintaining consistency in input features, and effectively visualizing complex diseases in low-dimensional spaces. In this proof-of-concept study, we demonstrated longitudinal EHR as a valuable resource for deep phenotyping the pre-AOM period at per patient visit level. Our analysis revealed the presence of clusters with distinct clinical significance, which could have implications in AOM treatment options. Further research using larger, independent cohorts is necessary to validate the reproducibility and clinical relevance of these clusters, uncover more detailed substructures and corresponding AOM treatment responses. |
| Audience | Academic |
| Author | Ruan, Xiaoyang Murali, Sameer Wang, Liwei Wen, Andrew Lu, Shuyu Liu, Hongfang |
| Author_xml | – sequence: 1 givenname: Xiaoyang orcidid: 0009-0006-7085-744X surname: Ruan fullname: Ruan, Xiaoyang – sequence: 2 givenname: Shuyu orcidid: 0000-0001-8486-2246 surname: Lu fullname: Lu, Shuyu – sequence: 3 givenname: Liwei orcidid: 0000-0001-9970-8604 surname: Wang fullname: Wang, Liwei – sequence: 4 givenname: Andrew orcidid: 0000-0001-9090-8028 surname: Wen fullname: Wen, Andrew – sequence: 5 givenname: Sameer orcidid: 0000-0003-2481-9910 surname: Murali fullname: Murali, Sameer – sequence: 6 givenname: Hongfang orcidid: 0000-0003-2570-3741 surname: Liu fullname: Liu, Hongfang |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40834423$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkt1qVDEQx4NU7Id9BTkggjdb83U-4o3UWm2hUtH2OuQkk92Uc5LTJCvsne_gG_okZrtt6ULJRYbJb_6ZPzP7aMcHDwgdEnxEiWg-tJhw_ALtEc66Wde1ZOdJvIv2U7rBmGIuyCu0y3HHOKdsD11_AZiqHwvwIa8m5-dVsNVlD8nl1cfqdACdY_BOV2eghryofoIO0fz78_ezSmCqKxinENVQfQ8GhnX5r7w0q9fopVVDgsP7-wBdfz29OjmbXVx-Oz85vphp3rA8I0JgRZsGSE9qTYyxdde3tFZUNTUYYZTAzDaM2t4IwAREDxos44QysD1lB-h8o2uCupFTdKOKKxmUk3eJEOdSxez0ALIVpHhW2gJTxTrpAWPGKC-xwpaxovVpozUt-xGMBp-LsS3R7RfvFnIefsvSTMsY5kXh_b1CDLdLSFmOLmkYBuUhLJMsvzVMiJaKgr7doHNVenPehiKp17g87hrOSU05KdTRM1Q5Bkany_ytK_mtgjdPPTw2_zDvArzbADqGlCLYR4Rgud4jebdH7D_Jerfx |
| Cites_doi | 10.1001/jama.1986.03380010055024 10.1136/bmjopen-2020-046407 10.1093/jamia/ocad247 10.1016/j.jand.2015.10.031 10.1056/NEJMoa012437 10.1101/2024.12.02.24318314 10.1016/j.celrep.2013.03.018 10.1097/MNH.0000000000000727 10.3389/fendo.2023.1326546 10.1001/jamasurg.2017.5025 10.1038/s41366-021-01017-8 10.1002/oby.23120 10.1007/5584_2016_84 10.1503/cmaj.191707 10.1186/s40537-020-00385-8 10.1007/s11695-016-2325-7 10.1007/s11154-023-09804-6 10.1007/s11154-023-09808-2 10.1016/j.eclinm.2023.101882 10.1016/S2213-8587(25)00036-1 10.1038/ijo.2009.2 10.1016/j.eclinm.2023.101923 10.1007/s11154-023-09829-x 10.1001/jama.2022.9009 10.3322/caac.21441 10.23750/abm.v90i10-S.8766 10.3390/healthcare6030073 10.1002/oby.23812 10.1210/clinem/dgaa285 10.1038/sj.ijo.0801941 10.1371/journal.pcbi.1009826 10.1177/2333794X19891305 10.1016/j.molmet.2016.11.003 10.3389/frai.2022.842306 10.1001/jamasurg.2013.3654 10.1111/dom.15765 10.1053/j.ajkd.2020.08.016 10.1016/j.fertnstert.2017.06.026 10.1056/NEJMsa1909301 10.1007/s11154-023-09796-3 10.4135/9781412971942 10.1007/s11695-016-2143-y 10.7759/cureus.30535 10.1016/j.rbmo.2019.04.017 10.2337/db13-1459 10.1371/journal.pdig.0000307 10.3390/informatics7020017 10.1002/oby.22727 10.1097/DSS.0000000000000209 10.1056/NEJMoa2206038 10.1007/s11154-023-09813-5 10.1136/bmjopen-2022-061251 10.1111/eci.13811 10.1002/osp4.574 10.3390/nu16203562 10.1016/j.soard.2018.02.027 10.1002/oby.22054 10.3390/electronics8111235 10.1016/j.jbi.2020.103433 10.1038/s41598-018-24271-9 10.1038/nature20784 10.1002/oby.22142 10.1503/cmaj.110387 10.1056/NEJM198601233140401 10.1186/s12859-023-05595-4 |
| ContentType | Journal Article |
| Copyright | Xiaoyang Ruan, Shuyu Lu, Liwei Wang, Andrew Wen, Sameer Murali, Hongfang Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org). COPYRIGHT 2025 Journal of Medical Internet Research Copyright ©Xiaoyang Ruan, Shuyu Lu, Liwei Wang, Andrew Wen, Sameer Murali, Hongfang Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org) 2025 |
| Copyright_xml | – notice: Xiaoyang Ruan, Shuyu Lu, Liwei Wang, Andrew Wen, Sameer Murali, Hongfang Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org). – notice: COPYRIGHT 2025 Journal of Medical Internet Research – notice: Copyright ©Xiaoyang Ruan, Shuyu Lu, Liwei Wang, Andrew Wen, Sameer Murali, Hongfang Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org) 2025 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM DOA |
| DOI | 10.2196/70140 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed 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) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Open Access Full Text 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: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Library & Information Science |
| EISSN | 1438-8871 |
| EndPage | e70140 |
| ExternalDocumentID | oai_doaj_org_article_791834acfe3a4421be003324442a0f33 PMC12373304 A864415241 40834423 10_2196_70140 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NLM NIH HHS grantid: R01 LM011934 |
| GroupedDBID | --- .4I .DC 29L 2WC 36B 53G 5GY 5VS 77I 77K 7RV 7X7 8FI 8FJ AAFWJ AAKPC AAWTL AAYXX ABDBF ABIVO ABUWG ACGFO ADBBV AEGXH AENEX AFFHD AFKRA AFPKN AIAGR ALMA_UNASSIGNED_HOLDINGS ALSLI AOIJS BAWUL BCNDV BENPR CCPQU CITATION CNYFK CS3 DIK DU5 DWQXO E3Z EAP EBD EBS EJD ELW EMB EMOBN ESX F5P FRP FYUFA GROUPED_DOAJ GX1 HMCUK HYE IAO ICO IEA IHR INH ISN ITC KQ8 M1O M48 NAPCQ OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PPXIY PQQKQ PRQQA RNS RPM SJN SV3 TR2 UKHRP XSB CGR CUY CVF ECM EIF NPM PUEGO 7X8 5PM |
| ID | FETCH-LOGICAL-c463t-1990a266e1b15c1ddf58b725a2a65ed9da903f632fbd9e01e9becef34123efb23 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001554919100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1438-8871 1439-4456 |
| IngestDate | Fri Oct 03 12:52:18 EDT 2025 Tue Nov 04 02:05:50 EST 2025 Sat Nov 01 14:16:40 EDT 2025 Thu Nov 27 00:19:55 EST 2025 Tue Nov 25 03:42:28 EST 2025 Thu Sep 04 05:05:18 EDT 2025 Sat Nov 29 07:36:38 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 25 |
| Keywords | precision medicine phenotyping EHR anti-obesity medication obesity |
| Language | English |
| License | Xiaoyang Ruan, Shuyu Lu, Liwei Wang, Andrew Wen, Sameer Murali, Hongfang Liu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org). This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c463t-1990a266e1b15c1ddf58b725a2a65ed9da903f632fbd9e01e9becef34123efb23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0009-0006-7085-744X 0000-0001-9970-8604 0000-0003-2481-9910 0000-0001-9090-8028 0000-0003-2570-3741 0000-0001-8486-2246 |
| OpenAccessLink | https://doaj.org/article/791834acfe3a4421be003324442a0f33 |
| PMID | 40834423 |
| PQID | 3246399729 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_791834acfe3a4421be003324442a0f33 pubmedcentral_primary_oai_pubmedcentral_nih_gov_12373304 proquest_miscellaneous_3246399729 gale_infotracmisc_A864415241 gale_infotracacademiconefile_A864415241 pubmed_primary_40834423 crossref_primary_10_2196_70140 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-08-20 |
| PublicationDateYYYYMMDD | 2025-08-20 |
| PublicationDate_xml | – month: 08 year: 2025 text: 2025-08-20 day: 20 |
| PublicationDecade | 2020 |
| PublicationPlace | Canada |
| PublicationPlace_xml | – name: Canada – name: Toronto, Canada |
| PublicationTitle | Journal of medical Internet research |
| PublicationTitleAlternate | J Med Internet Res |
| PublicationYear | 2025 |
| Publisher | Journal of Medical Internet Research JMIR Publications |
| Publisher_xml | – name: Journal of Medical Internet Research – name: JMIR Publications |
| References | Apovian (R22); 27 Jastreboff (R18); 387 Wharton (R25); 192 Yárnoz-Esquiroz (R46); 52 Che (R45); 8 Stunkard (R5); 314 Garvey (R28); 28 Datta (R65); 2 R63 R67 R69 Chintam (R52); 77 Prasad (R51); 14 Barres (R10); 3 Brissman (R16); 11 Ward (R3); 381 R1 Nilsson (R12); 63 Salmón-Gómez (R47); 24 Adams (R61); 31 Sanyaolu (R2); 6 Keller (R11); 6 R8 Acosta (R27); 29 Ye (R55); 13 Trang (R37); 24 Loftus (R70); 5 Parvathareddy (R53); 30 Sharma (R26); 33 Zhang (R71); 24 Reich (R43); 31 Courcoulas (R15); 153 Alelyani (R68); 8 Raynor (R23); 116 R39 Preda (R38); 24 Alzoubi (R40); 8 Wahl (R13); 541 Aminian (R60); 327 Kaplan (R34); 26 (R44) 2009 Cena (R57); 105 Koliaki (R24); 6 Chaudhry (R50); 26 Suppl 5 Morton (R21); 26 Atlantis (R31); 12 Zheng (R54); 14 Paolacci (R6); 90 Abdullah (R66); 7 Segal (R9); 26 Hocking (R36); 24 Oral (R35); 346 Chang (R14); 149 Padwal (R29); 183 Cifuentes (R48); 58 Chakhtoura (R19); 58 Demark-Wahnefried (R59); 68 Aronne (R20); 8 Clapp (R17); 14 Practice Committee of the American Society for Reproductive Medicine. Electronic address: ASRM@asrm.org (R56); 108 Weng (R41); 105 Portincasa (R32); 24 Zeng (R42); 2006 Turner (R33); 26 Górczyńska-Kosiorz (R7); 16 Han (R58); 39 Yu (R64); 18 Stunkard (R4); 256 Hamrahian (R49); 956 Friedmann (R62); 41 Rodríguez-Flores (R30); 46 |
| References_xml | – volume: 256 start-page: 51 issue: 1 ident: R4 publication-title: JAMA doi: 10.1001/jama.1986.03380010055024 – volume: 11 issue: 3 ident: R16 article-title: Prevalence of insufficient weight loss 5 years after Roux-en-Y gastric bypass: metabolic consequences and prediction estimates: a prospective registry study publication-title: BMJ Open doi: 10.1136/bmjopen-2020-046407 – volume: 31 start-page: 583 issue: 3 ident: R43 article-title: OHDSI Standardized Vocabularies—a large-scale centralized reference ontology for international data harmonization publication-title: J Am Med Inform Assoc doi: 10.1093/jamia/ocad247 – volume: 116 start-page: 129 issue: 1 ident: R23 article-title: Position of the Academy of Nutrition and Dietetics: Interventions for the Treatment of Overweight and Obesity in Adults publication-title: J Acad Nutr Diet doi: 10.1016/j.jand.2015.10.031 – volume: 346 start-page: 570 issue: 8 ident: R35 article-title: Leptin-replacement therapy for lipodystrophy publication-title: N Engl J Med doi: 10.1056/NEJMoa012437 – ident: R39 doi: 10.1101/2024.12.02.24318314 – ident: R69 – volume: 3 start-page: 1020 issue: 4 ident: R10 article-title: Weight loss after gastric bypass surgery in human obesity remodels promoter methylation publication-title: Cell Rep doi: 10.1016/j.celrep.2013.03.018 – volume: 30 start-page: 516 issue: 5 ident: R53 article-title: Treatment options for managing obesity in chronic kidney disease publication-title: Curr Opin Nephrol Hypertens doi: 10.1097/MNH.0000000000000727 – volume: 14 ident: R54 article-title: Obesity and its impact on female reproductive health: unraveling the connections publication-title: Front Endocrinol doi: 10.3389/fendo.2023.1326546 – volume: 153 start-page: 427 issue: 5 ident: R15 article-title: Seven-Year Weight Trajectories and Health Outcomes in the Longitudinal Assessment of Bariatric Surgery (LABS) Study publication-title: JAMA Surg doi: 10.1001/jamasurg.2017.5025 – volume: 46 start-page: 661 issue: 3 ident: R30 article-title: The utility of the Edmonton Obesity Staging System for the prediction of COVID-19 outcomes: a multi-centre study publication-title: Int J Obes doi: 10.1038/s41366-021-01017-8 – volume: 29 start-page: 662 issue: 4 ident: R27 article-title: Selection of Antiobesity Medications Based on Phenotypes Enhances Weight Loss: A Pragmatic Trial in an Obesity Clinic publication-title: Obesity (Silver Spring) doi: 10.1002/oby.23120 – volume: 956 ident: R49 article-title: Hypertension in Chronic Kidney Disease publication-title: Adv Exp Med Biol doi: 10.1007/5584_2016_84 – volume: 192 start-page: E875 issue: 31 ident: R25 article-title: Obesity in adults: a clinical practice guideline publication-title: CMAJ doi: 10.1503/cmaj.191707 – volume: 8 start-page: 1 issue: 1 ident: R68 article-title: Stable bagging feature selection on medical data publication-title: J Big Data doi: 10.1186/s40537-020-00385-8 – volume: 27 start-page: 169 issue: 1 ident: R22 article-title: Two-Year Outcomes of Vagal Nerve Blocking (vBloc) for the Treatment of Obesity in the ReCharge Trial publication-title: Obes Surg doi: 10.1007/s11695-016-2325-7 – volume: 24 start-page: 775 issue: 5 ident: R37 article-title: Genetics and epigenetics in the obesity phenotyping scenario publication-title: Rev Endocr Metab Disord doi: 10.1007/s11154-023-09804-6 – volume: 24 start-page: 951 issue: 5 ident: R36 article-title: Individualised prescription of medications for treatment of obesity in adults publication-title: Rev Endocr Metab Disord doi: 10.1007/s11154-023-09808-2 – ident: R1 – volume: 58 ident: R19 article-title: Pharmacotherapy of obesity: an update on the available medications and drugs under investigation publication-title: EClinicalMedicine doi: 10.1016/j.eclinm.2023.101882 – volume: 13 start-page: 494 issue: 6 ident: R55 article-title: Association between maternal diabetes and neurodevelopmental outcomes in children: a systematic review and meta-analysis of 202 observational studies comprising 56·1 million pregnancies publication-title: Lancet Diabetes Endocrinol doi: 10.1016/S2213-8587(25)00036-1 – volume: 33 start-page: 289 issue: 3 ident: R26 article-title: A proposed clinical staging system for obesity publication-title: Int J Obes doi: 10.1038/ijo.2009.2 – volume: 58 ident: R48 article-title: Phenotype tailored lifestyle intervention on weight loss and cardiometabolic risk factors in adults with obesity: a single-centre, non-randomised, proof-of-concept study publication-title: EClinicalMedicine doi: 10.1016/j.eclinm.2023.101923 – volume: 24 start-page: 767 issue: 5 ident: R32 article-title: Phenotyping the obesities: reality or utopia? publication-title: Rev Endocr Metab Disord doi: 10.1007/s11154-023-09829-x – volume: 327 start-page: 2423 issue: 24 ident: R60 article-title: Association of Bariatric Surgery With Cancer Risk and Mortality in Adults With Obesity publication-title: JAMA doi: 10.1001/jama.2022.9009 – volume: 68 start-page: 64 issue: 1 ident: R59 article-title: Weight management and physical activity throughout the cancer care continuum publication-title: CA Cancer J Clin doi: 10.3322/caac.21441 – volume: 90 start-page: 87 issue: 10-S ident: R6 article-title: Mendelian non-syndromic obesity publication-title: Acta Biomed doi: 10.23750/abm.v90i10-S.8766 – volume: 6 issue: 3 ident: R24 article-title: Defining the Optimal Dietary Approach for Safe, Effective and Sustainable Weight Loss in Overweight and Obese Adults publication-title: Healthcare (Basel) doi: 10.3390/healthcare6030073 – volume: 31 start-page: 2386 issue: 9 ident: R61 article-title: Long-term cancer outcomes after bariatric surgery publication-title: Obesity (Silver Spring) doi: 10.1002/oby.23812 – volume: 105 start-page: e2695 issue: 8 ident: R57 article-title: Obesity, Polycystic Ovary Syndrome, and Infertility: A New Avenue for GLP-1 Receptor Agonists publication-title: J Clin Endocrinol Metab doi: 10.1210/clinem/dgaa285 – volume: 26 start-page: 437 issue: 4 ident: R9 article-title: Twins and virtual twins: bases of relative body weight revisited publication-title: Int J Obes Relat Metab Disord doi: 10.1038/sj.ijo.0801941 – volume: 18 issue: 1 ident: R64 article-title: AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments publication-title: PLoS Comput Biol doi: 10.1371/journal.pcbi.1009826 – volume: 6 ident: R2 article-title: Childhood and Adolescent Obesity in the United States: A Public Health Concern publication-title: Glob Pediatr Health doi: 10.1177/2333794X19891305 – volume: 6 start-page: 86 issue: 1 ident: R11 article-title: Genome-wide DNA promoter methylation and transcriptome analysis in human adipose tissue unravels novel candidate genes for obesity publication-title: Mol Metab doi: 10.1016/j.molmet.2016.11.003 – volume: 5 ident: R70 article-title: Phenotype clustering in health care: A narrative review for clinicians publication-title: Front Artif Intell doi: 10.3389/frai.2022.842306 – volume: 149 start-page: 275 issue: 3 ident: R14 article-title: The effectiveness and risks of bariatric surgery: an updated systematic review and meta-analysis, 2003-2012 publication-title: JAMA Surg doi: 10.1001/jamasurg.2013.3654 – volume: 26 Suppl 5 ident: R50 article-title: Chronic kidney disease in type 2 diabetes: The size of the problem, addressing residual renal risk and what we have learned from the CREDENCE trial publication-title: Diabetes Obes Metab doi: 10.1111/dom.15765 – ident: R63 – volume: 77 start-page: 427 issue: 3 ident: R52 article-title: Strategies to Treat Obesity in Patients With CKD publication-title: Am J Kidney Dis doi: 10.1053/j.ajkd.2020.08.016 – volume: 108 start-page: 426 issue: 3 ident: R56 article-title: Role of metformin for ovulation induction in infertile patients with polycystic ovary syndrome (PCOS): a guideline publication-title: Fertil Steril doi: 10.1016/j.fertnstert.2017.06.026 – volume: 381 start-page: 2440 issue: 25 ident: R3 article-title: Projected U.S. State-Level Prevalence of Adult Obesity and Severe Obesity publication-title: N Engl J Med doi: 10.1056/NEJMsa1909301 – volume: 24 start-page: 809 issue: 5 ident: R47 article-title: Relevance of body composition in phenotyping the obesities publication-title: Rev Endocr Metab Disord doi: 10.1007/s11154-023-09796-3 – year: 2009 ident: R44 article-title: Healthcare Cost and Utilization Project (HCUP) publication-title: Encyclopedia of Health Services Research doi: 10.4135/9781412971942 – volume: 26 start-page: 983 issue: 5 ident: R21 article-title: Effect of Vagal Nerve Blockade on Moderate Obesity with an Obesity-Related Comorbid Condition: the ReCharge Study publication-title: Obes Surg doi: 10.1007/s11695-016-2143-y – volume: 14 issue: 10 ident: R51 article-title: Chronic Kidney Disease: Its Relationship With Obesity publication-title: Cureus doi: 10.7759/cureus.30535 – volume: 39 start-page: 332 issue: 2 ident: R58 article-title: GLP-1 receptor agonists versus metformin in PCOS: a systematic review and meta-analysis publication-title: Reprod Biomed Online doi: 10.1016/j.rbmo.2019.04.017 – volume: 63 start-page: 2962 issue: 9 ident: R12 article-title: Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes publication-title: Diabetes doi: 10.2337/db13-1459 – volume: 2 issue: 8 ident: R65 article-title: MapperPlus: Agnostic clustering of high-dimension data for precision medicine publication-title: PLOS Digit Health doi: 10.1371/journal.pdig.0000307 – volume: 2006 ident: R42 publication-title: AMIA Annu Symp Proc – ident: R67 – volume: 7 start-page: 17 issue: 2 ident: R66 article-title: Visual Analytics for Dimension Reduction and Cluster Analysis of High Dimensional Electronic Health Records publication-title: Informatics (MDPI) doi: 10.3390/informatics7020017 – volume: 28 start-page: 484 issue: 3 ident: R28 article-title: Proposal for a Scientifically Correct and Medically Actionable Disease Classification System (ICD) for Obesity publication-title: Obesity (Silver Spring) doi: 10.1002/oby.22727 – volume: 41 start-page: 18 issue: 1 ident: R62 article-title: A review of the aesthetic treatment of abdominal subcutaneous adipose tissue: background, implications, and therapeutic options publication-title: Dermatol Surg doi: 10.1097/DSS.0000000000000209 – volume: 387 start-page: 205 issue: 3 ident: R18 article-title: Tirzepatide Once Weekly for the Treatment of Obesity publication-title: N Engl J Med doi: 10.1056/NEJMoa2206038 – volume: 24 start-page: 901 issue: 5 ident: R38 article-title: Obesity phenotypes and cardiovascular risk: From pathophysiology to clinical management publication-title: Rev Endocr Metab Disord doi: 10.1007/s11154-023-09813-5 – volume: 12 issue: 6 ident: R31 article-title: Development and internal validation of the Edmonton Obesity Staging System-2 Risk screening Tool (EOSS-2 Risk Tool) for weight-related health complications: a case-control study in a representative sample of Australian adults with overweight and obesity publication-title: BMJ Open doi: 10.1136/bmjopen-2022-061251 – volume: 52 issue: 7 ident: R46 article-title: “Obesities”: Position statement on a complex disease entity with multifaceted drivers publication-title: Eur J Clin Invest doi: 10.1111/eci.13811 – volume: 8 start-page: 363 issue: 3 ident: R20 article-title: Recent advances in therapies utilizing superabsorbent hydrogel technology for weight management: A review publication-title: Obes Sci Pract doi: 10.1002/osp4.574 – volume: 16 issue: 20 ident: R7 article-title: Exploring the Interplay of Genetics and Nutrition in the Rising Epidemic of Obesity and Metabolic Diseases publication-title: Nutrients doi: 10.3390/nu16203562 – volume: 14 start-page: 741 issue: 6 ident: R17 article-title: Long term (7 or more years) outcomes of the sleeve gastrectomy: a meta-analysis publication-title: Surg Obes Relat Dis doi: 10.1016/j.soard.2018.02.027 – volume: 26 start-page: 61 issue: 1 ident: R34 article-title: Perceptions of Barriers to Effective Obesity Care: Results from the National ACTION Study publication-title: Obesity (Silver Spring) doi: 10.1002/oby.22054 – volume: 8 start-page: 1235 issue: 11 ident: R40 article-title: A Review of Automatic Phenotyping Approaches using Electronic Health Records publication-title: Electronics (Basel) doi: 10.3390/electronics8111235 – volume: 105 ident: R41 article-title: Deep phenotyping: Embracing complexity and temporality-Towards scalability, portability, and interoperability publication-title: J Biomed Inform doi: 10.1016/j.jbi.2020.103433 – volume: 8 issue: 1 ident: R45 article-title: Recurrent Neural Networks for Multivariate Time Series with Missing Values publication-title: Sci Rep doi: 10.1038/s41598-018-24271-9 – volume: 541 start-page: 81 issue: 7635 ident: R13 article-title: Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity publication-title: Nature New Biol doi: 10.1038/nature20784 – volume: 26 start-page: 665 issue: 4 ident: R33 article-title: Current Knowledge of Obesity Treatment Guidelines by Health Care Professionals publication-title: Obesity (Silver Spring) doi: 10.1002/oby.22142 – volume: 183 start-page: E1059 issue: 14 ident: R29 article-title: Using the Edmonton obesity staging system to predict mortality in a population-representative cohort of people with overweight and obesity publication-title: CMAJ doi: 10.1503/cmaj.110387 – volume: 314 start-page: 193 issue: 4 ident: R5 article-title: An adoption study of human obesity publication-title: N Engl J Med doi: 10.1056/NEJM198601233140401 – volume: 24 issue: 1 ident: R71 article-title: PWSC: a novel clustering method based on polynomial weight-adjusted sparse clustering for sparse biomedical data and its application in cancer subtyping publication-title: BMC Bioinformatics doi: 10.1186/s12859-023-05595-4 – ident: R8 |
| SSID | ssj0020491 |
| Score | 2.4489024 |
| Snippet | Obesity affects approximately 40% of adults and 15%-20% of children and adolescents in the United States, and poses significant economic and psychosocial... Background Obesity affects approximately 40% of adults and 15%‐20% of children and adolescents in the United States, and poses significant economic and... Obesity affects approximately 40% of adults and 15%‐20% of children and adolescents in the United States, and poses significant economic and psychosocial... Abstract BackgroundObesity affects approximately 40% of adults and 15%‐20% of children and adolescents in the United States, and poses significant economic and... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database |
| StartPage | e70140 |
| SubjectTerms | Adolescent Adult Analysis Child Clinical Informatics Clinical Information and Decision Making Digital Biomarkers and Digital Phenotyping Drug therapy Electronic Health Records Electronic records Female Healthcare industry software Humans Hypoglycemic agents Incidence and Prevalence of Obesity Male Medical records Obesity Obesity - drug therapy Ontologies, Classifications, and Coding Original Paper Phenotype Precision Medicine |
| Title | Deep Phenotyping of Obesity: Electronic Health Record–Based Temporal Modeling Study |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40834423 https://www.proquest.com/docview/3246399729 https://pubmed.ncbi.nlm.nih.gov/PMC12373304 https://doaj.org/article/791834acfe3a4421be003324442a0f33 |
| Volume | 27 |
| WOSCitedRecordID | wos001554919100001&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 Open Access Full Text customDbUrl: eissn: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: DOA dateStart: 19990101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Library Science Database customDbUrl: eissn: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: M1O dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/libraryscience providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: 7RV dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content customDbUrl: eissn: 1438-8871 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020491 issn: 1438-8871 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1fa9RAEB-0ShFEtP5ptD1WEH06uv9ym_jW0ysK3hlKK-dT2Gx2aUFypb0W-uZ38Bv6SZxJ9o4LPvjiywZ2A9nMb3dnJpn5DcCbSglHPFZDbazExqihNSYdoukgrFGOc9fyzH4xs1k2n-fFRqkvignr6IE7wR2YHBedti54ZbWWovJUfgyVkpaWB9XyfKLVs3KmoquFdq_YhocU6IxL7MCQH9HTPC1B_9_H8IYe6sdIbiido8fwKFqL7LCb5RO445sd2I-5Buwti8lEJFwWd-kObE_j__KncPrR-wtWnPlmsbylzCi2CCzWAnjPJusSOKzLRmKdM_r7568xKreanXS8VT8YVUyjvHVGYYe3z-D0aHLy4dMwFlIYOj1SSwom4RY1sReVSJ2o65BmlZGplXaU-jqvbc5VGCkZqjr3XPgckfUBFZxUPlRSPYetZtH4XWDK-yzzNc9dja4hl9ZmQejgdYVXqUMCg5WQy4uOL6NEP4NQKFsUEhiT6NeDRG_ddiDoZQS9_BfoCbwj4ErahIiOszGXAOdIdFblYUZmXorWSQJ7vTtx87je8OsV9CUNUcRZ4xfXVyU-jYw39D0SeNEthfWcNafqJBKnkfUWSe-l-iPN-VnL3Y0SNfQJ6eX_EMMreCCpHDGnw24PtpaX134f7rub5fnV5QDumuNv1M5N22YDuDeezIrjQbtXsJ2Kr9hXfJ4W3_8AllYXVg |
| linkProvider | Directory of Open Access Journals |
| 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=Deep+Phenotyping+of+Obesity%3A+Electronic+Health+Record-Based+Temporal+Modeling+Study&rft.jtitle=Journal+of+medical+Internet+research&rft.au=Ruan%2C+Xiaoyang&rft.au=Lu%2C+Shuyu&rft.au=Wang%2C+Liwei&rft.au=Wen%2C+Andrew&rft.date=2025-08-20&rft.issn=1438-8871&rft.eissn=1438-8871&rft.volume=27&rft.spage=e70140&rft_id=info:doi/10.2196%2F70140&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1438-8871&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1438-8871&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1438-8871&client=summon |