Evidence map›Paper›PMID 40819183›Full record

ArticlePediatric research2026

Evaluating neonatal cord serum metabolome in association with adolescent cardiometabolic risk factors.

Elvira S Fleury, George D Papandonatos, Katherine E Manz, Kurt Pennell, Amber M Hall, Aimin Chen, Jessie P Buckley, Kimberly Yolton, Kim M Cecil, Bruce P Lanphear and 3 more

Abstract read
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In one paragraph

Article in Pediatric research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Elvira S Fleury *Department of Epidemiology, Brown University, Providence, RI, US.
George D Papandonatos *Department of Biostatistics, Brown University, Providence, RI, US.
Katherine E ManzDepartment of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, US.
Kurt PennellSchool of Engineering, Brown University, Providence, RI, US.
Amber M HallDepartment of Epidemiology, Brown University, Providence, RI, US.
Aimin ChenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, US.
Jessie P BuckleyDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, US.
Kimberly YoltonDepartment of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, US.
Kim M CecilDepartment of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, US.
Bruce P LanphearFaculty of Health Sciences, Simon Fraser University, Vancouver, BC, Canada.
Charles B EatonDepartment of Family Medicine and Epidemiology, Alpert Medical School, Brown University, Providence, RI, US.
Douglas I WalkerGangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, US.
Joseph M BraunDepartment of Epidemiology, Brown University, Providence, RI, US. joseph_braun_1@brown.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRisk factors for cardiometabolic disease may have fetal origins, but the biological pathways linking gestational conditions to these risk factors are only partially understood.

methodsAmong 145 Cincinnati-based HOME Study mother-child dyads, we detected 14,384 cord serum metabolic features using liquid chromatography high-resolution mass spectrometry. We measured cardiometabolic risk factors, including visceral fat, serum triglyceride, high-density lipoprotein cholesterol (HDL), leptin, adiponectin, insulin concentration, glucose, and systolic blood pressure (SBP) at age 12 years. Using sparse Partial Least Squares Regression (sPLS-R), we simultaneously modeled the association of metabolic features with all 8 risk factors. We prioritized features with the highest sPLS-R-derived CM risk factor correlations for metabolic pathway enrichment analysis.

resultsWe identified two groups of cardiometabolic risk factors in adolescents maximally associated with neonatal metabolic features. The first was visceral fat, triglycerides, HDL, insulin, and leptin; the second was glucose and SBP. The 178 metabolic features with the highest sPLS-R-derived feature-outcome correlations were enriched in 31 pathways related to short-chain fatty acid, vitamins C and B3, and amino acid metabolism, as well as glycolysis and gluconeogenesis.

conclusionsWe identified 31 pathways that may help elucidate underlying mechanisms between fetal environmental stressors and the development of cardiometabolic risk factors. IMPACT: Using non-targeted metabolomics, we identified neonatal metabolic features linked to two groups of cardiometabolic risk factors in adolescents, suggesting distinct early-life CM risk trajectories and adolescent subphenotypes. One cardiometabolic group was characterized by higher visceral fat, triglycerides, insulin, leptin, as well as lower HDL; the other group was related to elevated glucose and systolic blood pressure. Using a variable selection and data-dimension reduction technique, these two groups were associated with 178 metabolic features and 31 biological pathways related to short-chain fatty acid, vitamins C and B3, and amino acid metabolism, as well as glycolysis and gluconeogenesis.

Indexed as

Cardiometabolic Risk FactorsFetal BloodMetabolomeAdolescentBlood GlucoseBlood PressureChildFemaleHumansInfant, NewbornInsulinIntra-Abdominal FatLeptinMaleMetabolomicsPregnancyBlood GlucoseInsulinLeptinTriglycerides

Identifiers

PMID40819183

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.