{"doi":"10.2337/db21-1093","title":"Predictive Metabolomic Markers in Early to Mid-pregnancy for Gestational Diabetes Mellitus: A Prospective Test and Validation Study","abstract":"Gestational diabetes mellitus (GDM) predisposes pregnant individuals to perinatal complications and long-term diabetes and cardiovascular diseases. We developed and validated metabolomic markers for GDM in a prospective test-validation study. In a case-control sample within the PETALS cohort (GDM n = 91 and non-GDM n = 180; discovery set), a random PETALS subsample (GDM n = 42 and non-GDM n = 372; validation set 1), and a case-control sample within the GLOW trial (GDM n = 35 and non-GDM n = 70; validation set 2), fasting serum untargeted metabolomics were measured by gas chromatography/time-of-flight mass spectrometry. Multivariate enrichment analysis examined associations between metabolites and GDM. Ten-fold cross-validated LASSO regression identified predictive metabolomic markers at gestational weeks (GW) 10-13 and 16-19 for GDM. Purinone metabolites at GW 10-13 and 16-19 and amino acids, amino alcohols, hexoses, indoles, and pyrimidine metabolites at GW 16-19 were positively associated with GDM risk (false discovery rate <0.05). A 17-metabolite panel at GW 10-13 outperformed the model using conventional risk factors, including fasting glycemia (area under the curve: discovery 0.871 vs. 0.742, validation 1 0.869 vs. 0.731, and validation 2 0.972 vs. 0.742; P < 0.01). Similar results were observed with a 13-metabolite panel at GW 17-19. Dysmetabolism is present early in pregnancy among individuals progressing to GDM. Multimetabolite panels in early pregnancy can predict GDM risk beyond conventional risk factors.","journal":"Diabetes","year":2022,"id":241509,"datarank":1.4402742697488184,"base_score":3.7376696182833684,"endowment":3.7376696182833684,"self_citation_contribution":0.5606504427425053,"citation_network_contribution":0.8796238270063133,"self_endowment_contribution":0.5606504427425053,"citer_contribution":0.8796238270063133,"corpus_percentile":null,"corpus_rank":null,"citation_count":41,"citer_count":40,"citers_with_citation_signal":30,"citers_with_endowment":30,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8465,"is_data_producer":true,"deposit_databanks":{"figshare":["10.2337/figshare.19609845"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":245216,"name":"Dinesh Kumar Barupal","orcid":"0000-0002-9954-8628","position":1,"is_corresponding":false},{"id":720597,"name":"Amanda L. Ngo","orcid":"0000-0002-4976-3469","position":2,"is_corresponding":false},{"id":322968,"name":"Charles P. Quesenberry","orcid":"0000-0001-8272-9126","position":3,"is_corresponding":false},{"id":478162,"name":"Juanran Feng","orcid":null,"position":4,"is_corresponding":false},{"id":6363,"name":"Oliver Fiehn","orcid":"0000-0002-6261-8928","position":5,"is_corresponding":false},{"id":381696,"name":"Assiamira Ferrara","orcid":"0000-0002-7505-4826","position":6,"is_corresponding":false},{"id":322966,"name":"Yeyi Zhu","orcid":"0000-0002-7296-738X","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-19T00:23:00.865267Z","pmid":"35532743","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":[]}