27337 Characterizing Temporal Patterns in Glucose Dysregulation Following SARS-CoV-2 Infection
ABSTRACT IMPACT: Understanding the longitudinal glucose changes following SARS-CoV-2 infection can inform point-of-care guidelines and elucidate the viral hypothesis of diabetes mellitus pathogenesis. OBJECTIVES/GOALS: Hyperglycemia has emerged as an important manifestation of SARS-CoV-2 infection i...
Saved in:
| Published in: | Journal of clinical and translational science Vol. 5; no. s1; p. 46 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Cambridge, UK
Cambridge University Press
01.03.2021
|
| Subjects: | |
| ISSN: | 2059-8661, 2059-8661 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | ABSTRACT IMPACT: Understanding the longitudinal glucose changes following SARS-CoV-2 infection can inform point-of-care guidelines and elucidate the viral hypothesis of diabetes mellitus pathogenesis. OBJECTIVES/GOALS: Hyperglycemia has emerged as an important manifestation of SARS-CoV-2 infection in both diabetic and non-diabetic patients. Whether clinically-detectable glycemic changes persist following SARS-CoV-2 infection remain to be elucidated. This work aims to characterize temporal patterns in glucose dysregulation following SARS-CoV-2 infection. METHODS/STUDY POPULATION: Electronic health records of patients with a diagnosis of COVID-19, positive laboratory test for SARS-CoV-2, and negative history of Diabetes Mellitus prior to infection were extracted from the TriNetX database. 7,502 patients with at least one blood glucose value 2 years to 2 weeks before, 2 weeks before to 2 weeks after, and 2 weeks after to 1 year after COVID-19 diagnosis were used for analysis. Temporal patterns are characterized by training state-of-the-art clustering algorithms, including fuzzy short time-series clustering, k-means for longitudinal data, and spectral clustering. Clustering performance is evaluated using internal evaluation metrics of the Silhouette coefficient, Calinski-Harabasz score, and Davies Bouldin index. RESULTS/ANTICIPATED RESULTS: Based on the success of prior clustering methods with random blood glucose measurements, we anticipate that the proposed time-series clustering algorithms will appropriately characterize temporal patterns of glycemic dysregulation. The best performing algorithm based on interval evaluation metrics will be selected for further analysis. Associations between blood glucose values and cluster membership will be evaluated using Kruskal-Wallis one-way ANOVA and effect size will be calculated using unbiased Cohen’s d. Clinical phenotypes for each cluster will be characterized in terms of current diagnoses, prior medication use, pertinent laboratory tests, and vital signs. DISCUSSION/SIGNIFICANCE OF FINDINGS: A clearer understanding of the longitudinal glucose changes following SARS-CoV-2 infection can elucidate clinically-detectable patterns of glycemic dysregulation, identify sub-phenotypes of patients who are more susceptive to glycemic dysregulation, and inform appropriate point-of-care guidelines. |
|---|---|
| AbstractList | ABSTRACT IMPACT: Understanding the longitudinal glucose changes following SARS-CoV-2 infection can inform point-of-care guidelines and elucidate the viral hypothesis of diabetes mellitus pathogenesis. OBJECTIVES/GOALS: Hyperglycemia has emerged as an important manifestation of SARS-CoV-2 infection in both diabetic and non-diabetic patients. Whether clinically-detectable glycemic changes persist following SARS-CoV-2 infection remain to be elucidated. This work aims to characterize temporal patterns in glucose dysregulation following SARS-CoV-2 infection. METHODS/STUDY POPULATION: Electronic health records of patients with a diagnosis of COVID-19, positive laboratory test for SARS-CoV-2, and negative history of Diabetes Mellitus prior to infection were extracted from the TriNetX database. 7,502 patients with at least one blood glucose value 2 years to 2 weeks before, 2 weeks before to 2 weeks after, and 2 weeks after to 1 year after COVID-19 diagnosis were used for analysis. Temporal patterns are characterized by training state-of-the-art clustering algorithms, including fuzzy short time-series clustering, k-means for longitudinal data, and spectral clustering. Clustering performance is evaluated using internal evaluation metrics of the Silhouette coefficient, Calinski-Harabasz score, and Davies Bouldin index. RESULTS/ANTICIPATED RESULTS: Based on the success of prior clustering methods with random blood glucose measurements, we anticipate that the proposed time-series clustering algorithms will appropriately characterize temporal patterns of glycemic dysregulation. The best performing algorithm based on interval evaluation metrics will be selected for further analysis. Associations between blood glucose values and cluster membership will be evaluated using Kruskal-Wallis one-way ANOVA and effect size will be calculated using unbiased Cohen’s d. Clinical phenotypes for each cluster will be characterized in terms of current diagnoses, prior medication use, pertinent laboratory tests, and vital signs. DISCUSSION/SIGNIFICANCE OF FINDINGS: A clearer understanding of the longitudinal glucose changes following SARS-CoV-2 infection can elucidate clinically-detectable patterns of glycemic dysregulation, identify sub-phenotypes of patients who are more susceptive to glycemic dysregulation, and inform appropriate point-of-care guidelines. |
| Author | Gouripeddi, Ramkiran Mistry, Sejal Facelli, Julio C. |
| AuthorAffiliation | Department of Biomedical Informatics University of Utah |
| AuthorAffiliation_xml | – name: Department of Biomedical Informatics University of Utah |
| Author_xml | – sequence: 1 givenname: Sejal surname: Mistry fullname: Mistry, Sejal – sequence: 2 givenname: Ramkiran surname: Gouripeddi fullname: Gouripeddi, Ramkiran – sequence: 3 givenname: Julio C. surname: Facelli fullname: Facelli, Julio C. |
| BookMark | eNpVkUtLw0AUhQdR8LnzB-QHmDqPzCMbQeqrICi2unS4mbmpkXSmTFJFf72pFdHVvZzD-Rbn7JPtEAMScszoiFGmT13fjTjlbCS52CJ7nMoyN0qx7T__LjnquldKKTNcKSH2yDPXQuhs_AIJXI-p-WzCPJvhYhkTtNk99IMYuqwJ2XW7crHD7OKjSzhftdA3MWRXsW3j-zo0PX-Y5uP4lPNsEmp0a_uQ7NTQdnj0cw_I49XlbHyT395dT8bnt7ljpRI5lAVFCWXFHcVCombaKFdDKYHXWjHHjAdkXoLyrmBVZSrtZQGy9hXnBRUHZLLh-givdpmaBaQPG6Gx30JMcwupb1yLlnuUrCgNUMDCQQWaUVFKLr2uAUszsM42rOWqWqB3GPqhin_Q_05oXuw8vlljuFaGD4CTDcCl2A1d1b9ZRu16KztsZddb2WEr8QU2B4pi |
| ContentType | Journal Article |
| Copyright | The Association for Clinical and Translational Science 2021 2021 The Association for Clinical and Translational Science |
| Copyright_xml | – notice: The Association for Clinical and Translational Science 2021 2021 The Association for Clinical and Translational Science |
| DBID | AAYXX CITATION 5PM DOA |
| DOI | 10.1017/cts.2021.523 |
| DatabaseName | CrossRef PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2059-8661 |
| EndPage | 46 |
| ExternalDocumentID | oai_doaj_org_article_2de51498a0ae4caba71039525d7fae98 PMC8827682 10_1017_cts_2021_523 |
| GroupedDBID | 09C 09E 0R~ 8FE 8FH AABES AABWE AAGFV AAKTX AASVR AAYXX ABGDZ ABQTM ABROB ABVZP ABXHF ACAJB ACBEK ACDLN ACGFS ACUIJ ADAZD ADBBV ADDNB ADKIL ADOVH ADVJH AEBAK AEHGV AEMTJ AEYHU AFFHD AFKQG AFKRA AFLVW AFZFC AGABE AGJUD AHIPN AHQXX AHRGI AIGNW AIHIV AIOIP AJCYY AKMAY ALMA_UNASSIGNED_HOLDINGS ANPSP AQJOH ARCSS AUXHV AZGZS BBLKV BBNVY BCNDV BENPR BHPHI BLZWO BMAJL BRIRG CBIIA CCPQU CCQAD CFAFE CITATION CJCSC DOHLZ GROUPED_DOAJ HCIFZ HYE IKXGN IOEEP IPYYG JHPGK JKPOH JQKCU JVRFK KCGVB KFECR LK8 M7P M~E NIKVX OK1 PHGZM PHGZT PQGLB RCA ROL RPM S6U SAAAG T9M WFFJZ ZYDXJ 5PM |
| ID | FETCH-LOGICAL-c1963-a940e5a9b2c0e45e71786cfa95a2f761c18dae1d5a6dc41bb8b7d54a5fdb22403 |
| IEDL.DBID | DOA |
| ISSN | 2059-8661 |
| IngestDate | Tue Oct 14 19:05:25 EDT 2025 Tue Nov 04 01:37:07 EST 2025 Sat Nov 29 01:40:29 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | s1 |
| Language | English |
| License | http://creativecommons.org/licenses/by/4.0 This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c1963-a940e5a9b2c0e45e71786cfa95a2f761c18dae1d5a6dc41bb8b7d54a5fdb22403 |
| OpenAccessLink | https://doaj.org/article/2de51498a0ae4caba71039525d7fae98 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_2de51498a0ae4caba71039525d7fae98 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8827682 crossref_primary_10_1017_cts_2021_523 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-03-01 |
| PublicationDateYYYYMMDD | 2021-03-01 |
| PublicationDate_xml | – month: 03 year: 2021 text: 2021-03-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Cambridge, UK |
| PublicationPlace_xml | – name: Cambridge, UK |
| PublicationTitle | Journal of clinical and translational science |
| PublicationYear | 2021 |
| Publisher | Cambridge University Press |
| Publisher_xml | – name: Cambridge University Press |
| SSID | ssj0001826633 |
| Score | 2.1359406 |
| Snippet | ABSTRACT IMPACT: Understanding the longitudinal glucose changes following SARS-CoV-2 infection can inform point-of-care guidelines and elucidate the viral... |
| SourceID | doaj pubmedcentral crossref |
| SourceType | Open Website Open Access Repository Index Database |
| StartPage | 46 |
| SubjectTerms | Data Science/Biostatistics/Informatics Precision Medicine |
| Title | 27337 Characterizing Temporal Patterns in Glucose Dysregulation Following SARS-CoV-2 Infection |
| URI | https://pubmed.ncbi.nlm.nih.gov/PMC8827682 https://doaj.org/article/2de51498a0ae4caba71039525d7fae98 |
| Volume | 5 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAEN databaseName: Cambridge University Press Wholly Gold Open Access Journals customDbUrl: eissn: 2059-8661 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001826633 issn: 2059-8661 databaseCode: IKXGN dateStart: 20170201 isFulltext: true titleUrlDefault: http://journals.cambridge.org/action/login providerName: Cambridge University Press – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2059-8661 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001826633 issn: 2059-8661 databaseCode: DOA dateStart: 20170101 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: 2059-8661 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001826633 issn: 2059-8661 databaseCode: M~E dateStart: 20160101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2059-8661 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001826633 issn: 2059-8661 databaseCode: M7P dateStart: 20170201 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2059-8661 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001826633 issn: 2059-8661 databaseCode: BENPR dateStart: 20170201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrZ07T8MwEMctVDGwIBAgykseYDQkdhwnYyktIEFV0VJ1InL8gEooRW0AwcBnx49QNSwsLBnyuuQu0fnif34HwDHBmljKNyKaEhRRU7ByqQMksdQ0ThhTrkvE6Ib1esl4nPaXWn1ZTZjHA3vHnWGpTE5PEx5wFQmec2YnLymmkmmuUvebb8DSpWLKfV0xo-aYkErpbhnRorRwbhyeUkxqOcih-n9LIpdyTHcDrFeDQ9jyF7UJVlSxBR5M6icMthdk5U-TbeDQI6WeYd8BMos5nBTw0uvP4cWHMfpYNeaCXRPq6bs9aNC6G6D2dIQwvK5EWMU2uO92hu0rVHVFQMK-LYinUaAoT3MsAhVRZeqxJBaap5RjzeJQhInkKpSUx1JEYZ4nOZM04lTL3OZvsgMaxbRQuwBqKrDkhHAWyEhZklxo5wEtsDA3JnATnPz4KXvx8IvMq8JYZvyZWX9mxp9NcG6duNjHIqvdChPIrApk9lcgm4DVQlA7W31LMXlyIGxTHZhqCe_9h_l9sGZvx8vLDkCjnL2qQ7Aq3srJfHbkni6zvP3qfAOzItgV |
| 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=27337+Characterizing+Temporal+Patterns+in+Glucose+Dysregulation+Following+SARS-CoV-2+Infection&rft.jtitle=Journal+of+clinical+and+translational+science&rft.au=Sejal+Mistry&rft.au=Ramkiran+Gouripeddi&rft.au=Julio+C.+Facelli&rft.date=2021-03-01&rft.pub=Cambridge+University+Press&rft.eissn=2059-8661&rft.volume=5&rft.spage=46&rft.epage=46&rft_id=info:doi/10.1017%2Fcts.2021.523&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_2de51498a0ae4caba71039525d7fae98 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2059-8661&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2059-8661&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2059-8661&client=summon |