{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T06:02:44Z","timestamp":1784872964856,"version":"3.55.0"},"reference-count":66,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Coronavirus disease 2019 (COVID-19) shows prominent clinical heterogeneity, presenting two distinct clinical phenotypes including asymptomatic infection and severe symptomatic disease, yet the molecular mechanisms driving such phenotypic differences remain unclear. This study aimed to dissect the molecular basis of divergent COVID-19 clinical manifestations by combining serum proteomics and Mendelian randomization causal inference methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Serum samples from healthy controls, asymptomatic patients (acute and recovery phases), and symptomatic patients (incubation, acute, recovery phases) were subjected to label-free quantitative proteomics to screen stage-specific protein signatures. Functional enrichment analysis was conducted to mine critical biological pathways. Two MR strategies, two-sample MR and SMR, were utilized to infer causal associations between circulating metabolic proteins\/traits and COVID-19 susceptibility. This retrospective research did not involve clinical trials (Clinical trial number: Not applicable).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In total, 662 quantifiable serum proteins were detected. Principal component analysis revealed partial separation but obvious overlap across clinical subgroups. Asymptomatic patients only exhibited mild dysregulation of coagulation and innate immune-related proteins, whereas symptomatic patients displayed widespread disorders in coagulation, immunity, metabolism and tissue homeostasis. Five conserved hypoxia and metabolic regulatory proteins (ALDOA, ALDOB, LDHA, LDHB, TFRC) were differentially expressed in both phenotypes. Glycolysis and HIF-1 signaling pathways were disturbed in both groups, with more severe protein expression changes in symptomatic individuals. SMR analysis detected a nominally significant association between ALDOB cis-eQTL variants and COVID-19 risk (\n                      <jats:italic>OR<\/jats:italic>\n                      = 1.249, 95% CI: 1.030\u20131.513,\n                      <jats:italic>P<\/jats:italic>\n                      = 0.024). Two-sample MR indicated that higher circulating glycerol was weakly associated with COVID-19 infection (\n                      <jats:italic>OR<\/jats:italic>\n                      = 1.14, 95% CI: 1.01\u20131.28,\n                      <jats:italic>P<\/jats:italic>\n                      = 0.027), while global glucose homeostasis traits showed no significant causal links. All MR results were stable after heterogeneity and pleiotropy sensitivity tests.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Varied severity of host metabolic-immune dysregulation accounts for different COVID-19 clinical phenotypes. Asymptomatic carriers only experience mild coagulation-immune and metabolic disturbances, whereas symptomatic patients suffer substantially aggravated dysfunction of core metabolic pathways, which provides proteomic and causal evidence for the heterogeneous clinical manifestations of COVID-19.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmicb.2026.1852103","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T05:35:37Z","timestamp":1784871337000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Metabolic reprogramming is associated with symptomatic COVID-19: a serum proteomics and causal inference study identifying ALDOB and glycerol as candidate metabolic correlates"],"prefix":"10.3389","volume":"17","author":[{"given":"Nana","family":"Guo","sequence":"first","affiliation":[{"name":"Hebei Provincial Center for Disease Control and Prevention","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianlei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Public Health, North China University of Science and Technology","place":["Tangshan, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Public Health, Hebei University","place":["Baoding, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Hebei Provincial Center for Disease Control and Prevention","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caixiao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Hebei Provincial Center for Disease Control and Prevention","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghao","family":"Geng","sequence":"additional","affiliation":[{"name":"Hebei Provincial Center for Disease Control and Prevention","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentao","family":"Wu","sequence":"additional","affiliation":[{"name":"Hebei Provincial Center for Disease Control and Prevention","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Han","sequence":"additional","affiliation":[{"name":"Hebei Provincial Center for Disease Control and Prevention","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[{"name":"Hebei Provincial Center for Disease Control and Prevention","place":["Shijiazhuang, Hebei, China"]},{"name":"School of Public Health, North China University of Science and Technology","place":["Tangshan, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"108347","DOI":"10.1016\/j.diabres.2020.108347","article-title":"Diabetes and COVID-19: a systematic review on the current evidences","volume":"166","author":"Abdi","year":"2020","journal-title":"Diabetes Res. 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