{"doi":"10.1210/jendso/bvad029","title":"Association Between Metabolic Syndrome Inflammatory Biomarkers and COVID-19 Severity","abstract":"Context: Metabolic syndrome (MetS) is associated with increased risk of severe COVID-19. MetS inflammatory biomarkers share similarities with those of COVID-19, yet this association is poorly explored. Objective: Biomarkers of COVID-19 patients with and without MetS, the combination of diabetes, hypertension, obesity, and/or dyslipidemia, were analyzed to identify biological predictors of COVID-19 severity. Methods: In this prospective observational study, at a large academic emergency department in Boston, Massachusetts, clinical and proteomics data were analyzed from March 24 to April 30, 2020. Patients age ≥18 with a clinical concern for COVID-19 upon arrival and acute respiratory distress were included. The main outcome was severe COVID-19 as defined using World Health Organization COVID-19 outcomes scores ≤4, which describes patients who died, required invasive mechanical ventilation, or required supplemental oxygen. Results: Among 155 COVID-19 patients, 90 (58.1%) met the definition of MetS and 65 (41.9%) were identified as Control. The MetS cohort was more likely to have severe COVID-19 compared with the Control cohort (OR 2.67 [CI 1.09-6.55]). Biomarkers, including CXCL10 (OR 1.94 [CI 1.38-2.73]), CXCL9 (OR 1.79 [CI 1.09-2.93]), HGF (OR 3.30 [CI 1.65-6.58]), and IL6 (OR 2.09 [CI 1.49-2.94]) were associated with severe COVID-19. However, when stratified by MetS, only CXCL10 (OR 2.39 [CI 1.38-4.14]) and IL6 (OR 3.14 [CI 1.53-6.45]) were significantly associated with severe COVID-19. Conclusions: MetS-associated severe COVID-19 is characterized by an immune signature of elevated levels of CXCL10 and IL6. Clinical trials targeting CXCL10 or IL6 antagonism in this population may be warranted.","journal":"Journal of the Endocrine Society","year":2023,"id":384662,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8686,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":279970,"name":"Yuanhao Zu","orcid":null,"position":1,"is_corresponding":false},{"id":1149956,"name":"Farhad Ghamsari","orcid":"0000-0002-6893-7516","position":2,"is_corresponding":false},{"id":1152126,"name":"Janice Oh","orcid":"0000-0002-6621-8227","position":3,"is_corresponding":false},{"id":104951,"name":"Franck Mauvais‐Jarvis","orcid":"0000-0002-0874-0754","position":4,"is_corresponding":false},{"id":311565,"name":"Hui Zheng","orcid":"0000-0001-6725-1910","position":5,"is_corresponding":false},{"id":89099,"name":"Michael R. Filbin","orcid":"0000-0002-2588-7504","position":6,"is_corresponding":false},{"id":277943,"name":"Joshua L. Denson","orcid":"0000-0002-8654-7765","position":7,"is_corresponding":false},{"id":277937,"name":"Thaidan Pham","orcid":"0000-0003-1153-0632","position":0,"is_corresponding":true}],"reference_count":53,"raw_metadata":null,"created_at":"2026-07-19T01:17:37.110513Z","pmid":"36911320","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}