{"doi":"10.1111/ijpo.13172","title":"The predictive utility of the in utero exposome for childhood adiposity in independent and integrated frameworks","abstract":"OBJECTIVES: To assess the predictive potential of the in utero exposome in relation to childhood adiposity as indicated by body mass index z-scores (BMIz) and the fourth versus first quartile of % fat mass (FM) at median age of 4.6 years. METHODS: We leveraged data on clinical risk factors for childhood obesity during the perinatal period, along with cord blood per/polyfluoroalkyl substances (PFAS) and cord blood DNA methylation, in 268 mother-offspring pairs. We used the sparsity ranked LASSO penalized regression framework for each outcome and assessed model performance based on % variability explained for BMIz and area under the receiver operating characteristic curve (AUC) for the fourth versus first quartile of %FM. We employed cross-validation for model tuning and split-sample validation for model evaluation. RESULTS: Mean ± SD BMIz was 0.01 ± 1.1, %FM was 19.8 ± 6.34%. The optimal model for predicting BMIz explained 19.1% of the variability in the validation set and included only clinical characteristics: maternal pre-pregnancy BMI, paternal BMI, gestational weight gain, physical activity during pregnancy and child race/ethnicity. The optimal model for fourth versus first quartiles of %FM achieved an AUC of 0.82 ± 0.01 in the validation set, with the clinical features again emerging as the strongest predictors. CONCLUSION: In this study sample, perinatal chemical exposures and the epigenome have low utility in predicting childhood adiposity, beyond known clinical risk factors.","journal":"Pediatric Obesity","year":2024,"id":474158,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6551,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":646880,"name":"Ryan Peterson","orcid":"0000-0002-4650-5798","position":1,"is_corresponding":false},{"id":312591,"name":"Kylie K. Harrall","orcid":"0000-0003-4467-2282","position":2,"is_corresponding":false},{"id":1310748,"name":"Brandy Moon","orcid":null,"position":3,"is_corresponding":false},{"id":249641,"name":"Dana Dabelea","orcid":"0000-0001-9514-8929","position":4,"is_corresponding":false},{"id":294107,"name":"Katerina Kechris","orcid":"0000-0002-3725-5459","position":5,"is_corresponding":false},{"id":301092,"name":"Wei Perng","orcid":"0000-0001-8552-6850","position":6,"is_corresponding":false},{"id":1310364,"name":"Jonathan VanHawkins","orcid":"0009-0006-0389-0020","position":0,"is_corresponding":true}],"reference_count":21,"raw_metadata":null,"created_at":"2026-07-19T02:06:09.108857Z","pmid":"39327854","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":[]}