{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:36:37Z","timestamp":1783488997960,"version":"3.55.0"},"reference-count":24,"publisher":"Bioscientifica","issue":"4","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":347,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82270852"],"award-info":[{"award-number":["82270852"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"crossref","award":["21S31901800"],"award-info":[{"award-number":["21S31901800"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"crossref","award":["21ZR1450500"],"award-info":[{"award-number":["21ZR1450500"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"crossref","award":["JCYJ20220530145210024"],"award-info":[{"award-number":["JCYJ20220530145210024"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"crossref","award":["2023A1515012624"],"award-info":[{"award-number":["2023A1515012624"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"crossref","award":["2024A1515013260"],"award-info":[{"award-number":["2024A1515013260"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.bioscientifica.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,1]]},"abstract":"<jats:sec>\n                    <jats:title>Graphical abstract<\/jats:title>\n                    <jats:p\/>\n                    <jats:p>(Created in BioRender; https:\/\/BioRender.com\/o08n003). Abbreviations: GDM, gestational diabetes mellitus; NGT, normal glucose tolerance; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; LPC, lysophosphatidylcholine; LPE, lysophosphatidylethanolamine.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Abstract<\/jats:title>\n                    <jats:sec>\n                      <jats:title>Objective<\/jats:title>\n                      <jats:p>Women with gestational diabetes mellitus (GDM) often develop a metabolic memory that increases the risk of future metabolic disorders, even after blood glucose levels normalize following clinical intervention. However, the impact of this metabolic memory on susceptibility to SARS-CoV-2 remains unclear. Therefore, we aim to investigate the potential association between metabolic memory in GDM and susceptibility to SARS-CoV-2 infection.<\/jats:p>\n                    <\/jats:sec>\n                    <jats:sec>\n                      <jats:title>Methods<\/jats:title>\n                      <jats:p>We conducted a prospective cohort study with 1,675 pregnant women, including 197 (11.8%) with GDM. Postpartum SARS-CoV-2 infections were tracked via telephone follow-up and categorized into negative and positive groups. Logistic regression was used to explore risk factors for SARS-CoV-2 infection. Peripheral blood samples were collected from 30 GDM and 30 normal glucose-tolerant (NGT) pregnant women in three trimesters (T1, T2 and T3) for longitudinal untargeted metabolomics to identify GDM and SARS-CoV-2-associated metabolites. Limma package was applied to find differential expressed metabolites associated with SARS-CoV-2 infection and GDM.<\/jats:p>\n                    <\/jats:sec>\n                    <jats:sec>\n                      <jats:title>Results<\/jats:title>\n                      <jats:p>Among 1,675 women, 1,348 (80.5%) tested positive for SARS-CoV-2. GDM postpartum women had higher SARS-CoV-2 infection rates (88.3 vs 79.4%, P = 0.003) than NGT women. GDM was associated with SARS-CoV-2 infection (T2: OR (95% CI): 2.17 (1.26\u20133.54), P = 0.005; T3: OR (95% CI): 1.70 (1.03\u20132.82), P = 0.040). Compared to the SARS-CoV-2 negative group, the positive group exhibited elevated levels of allantoic acid, LPE (0:0\/22:6), LPC (15:0\/0:0) and 1-linoleoyl-sn-glycero-3-phosphorylcholine in T1 and T2, before clinical intervention. In T3, allantoic acid remained elevated post-intervention. A similar increase as described above was observed in the GDM compared to the NGT group. The tricarboxylic acid cycle was the sole overlapping enriched pathway in the SARS-CoV-2 positive versus negative group and the GDM versus NGT group. Cis-aconitic acid, a metabolite from this pathway, was elevated in T3 in the GDM group.<\/jats:p>\n                    <\/jats:sec>\n                    <jats:sec>\n                      <jats:title>Conclusion<\/jats:title>\n                      <jats:p>Compared to NGT, women with GDM are at a higher risk of postnatal SARS-CoV-2 infection. Metabolic memory from GDM may heighten susceptibility to SARS-CoV-2.<\/jats:p>\n                    <\/jats:sec>\n                  <\/jats:sec>","DOI":"10.1530\/ec-24-0681","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T13:52:44Z","timestamp":1740750764000},"update-policy":"https:\/\/doi.org\/10.1530\/crossmarkpolicy-10","source":"Crossref","is-referenced-by-count":4,"title":["Metabolic memory in gestational diabetes enhances SARS-CoV-2 susceptibility in postpartum women: a prospective cohort study integrated with longitudinal metabolomics"],"prefix":"10.1530","volume":"14","author":[{"given":"Zhangyan","family":"Li","sequence":"first","affiliation":[{"name":"Department of Endocrinology, Shanghai Fifth People\u2019s Hospital, Fudan University 1 , ,","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuhan","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Public Health (Shenzhen), 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Endocrinology, Shanghai Fifth People\u2019s Hospital, Fudan University 1 , ,","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinmei","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Shanghai Fifth People\u2019s Hospital, Fudan University 1 , ,","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaying","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University 2 , , ,","place":["Shenzhen, Guangdong, China"]},{"name":"School of Public Health (Shenzhen), Sun Yat-sen University 3 , , ,","place":["Guangzhou, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0810-7649","authenticated-orcid":true,"given":"Shufei","family":"Zang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Shanghai 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