Evidence map›Paper›PMID 32553738›Full record

ArticleMetabolism: clinical and experimental2020

A multi-omic analysis of birthweight in newborn cord blood reveals new underlying mechanisms related to cholesterol metabolism.

Rossella Alfano, Marc Chadeau-Hyam, Akram Ghantous, Pekka Keski-Rahkonen, Leda Chatzi, Almudena Espin Perez, Zdenko Herceg, Manolis Kogevinas, Theo M de Kok, Tim S Nawrot and 12 more

Open access · bronzeAbstract read
In one paragraph

Article in Metabolism: clinical and experimental, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
8.8field-weighted citation impact, top 2% of its field
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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.

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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

22 authors at 13 institutions in 8 countries.

Rossella AlfanoDepartment of Epidemiology and Biostatistics, The School of Public Health, Imperial College London, London, United Kingdom; Medical Research Council-Health Protection Agency Centre for Environment and Health, Imperial College London, London, United Kingdom; Centre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium.
Marc Chadeau-HyamDepartment of Epidemiology and Biostatistics, The School of Public Health, Imperial College London, London, United Kingdom; Medical Research Council-Health Protection Agency Centre for Environment and Health, Imperial College London, London, United Kingdom; Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, Utrecht, the Netherlands.
Akram GhantousInternational Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69008 Lyon, France.
Pekka Keski-RahkonenInternational Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69008 Lyon, France.
Leda ChatziDepartment of Preventive Medicine, University of Southern California, Los Angeles, CA 90007, United States; Department of Social Medicine, University of Crete, Heraklion, Crete, Greece.
Almudena Espin PerezDepartment of Biomedical Informatics Research, Stanford University, CA, United States.
Zdenko HercegInternational Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69008 Lyon, France.
Manolis KogevinasConsortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain; ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain.
Theo M de KokDepartment of Toxicogenomics, Maastricht University, Maastricht, the Netherlands.
Tim S NawrotCentre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium; Environment & Health Unit, Leuven University, Leuven, Belgium.
Alexei NovoloacaInternational Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69008 Lyon, France.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States.
Costanza PizziDepartment of Medical Sciences, University of Turin and CPO-Piemonte, Torino, Italy.
Nivonirina RobinotInternational Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69008 Lyon, France.
Franca RusconiUnit of Epidemiology, Anna Meyer Children's University Hospital, Florence, Italy.
Augustin ScalbertInternational Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69008 Lyon, France.
Jordi SunyerISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain.
Roel VermeulenDepartment of Epidemiology and Biostatistics, The School of Public Health, Imperial College London, London, United Kingdom; Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, Utrecht, the Netherlands.
Martine VrijheidConsortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain; ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain.
Paolo VineisDepartment of Epidemiology and Biostatistics, The School of Public Health, Imperial College London, London, United Kingdom; Medical Research Council-Health Protection Agency Centre for Environment and Health, Imperial College London, London, United Kingdom; Human Genetic Foundation (HuGeF), Turin, Italy.
Oliver RobinsonDepartment of Epidemiology and Biostatistics, The School of Public Health, Imperial College London, London, United Kingdom.
Michelle PlusquinDepartment of Epidemiology and Biostatistics, The School of Public Health, Imperial College London, London, United Kingdom; Medical Research Council-Health Protection Agency Centre for Environment and Health, Imperial College London, London, United Kingdom; Centre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium. Electronic address: michelle.plusquin@uhasselt.be.
Centre International de Recherche sur le Cancer · FRImperial College London · GBCenter for Research in Environmental Epidemiology · ESCentro de Investigación Biomédica en Red de Epidemiología y Salud Pública · ESHarvard University · USHasselt University · BEMaastricht University · NLMedical Research Council · GBMeyer Children's Hospital · ITStanford University · USUniversity of Crete · GRUniversity of Turin · ITUtrecht University · NL

Funding

Medical Research Council MR/S019669/1Medical Research Council MR/S03532X/1World Health Organization 001
6 · The paper itself

Abstract

backgroundBirthweight reflects in utero exposures and later health evolution. Despite existing studies employing high-dimensional molecular measurements, the understanding of underlying mechanisms of birthweight remains limited.

methodsTo investigate the systems biology of birthweight, we cross-sectionally integrated the methylome, the transcriptome, the metabolome and a set of inflammatory proteins measured in cord blood samples, collected from four birth-cohorts (n = 489). We focused on two sets of 68 metabolites and 903 CpGs previously related to birthweight and investigated the correlation structures existing between these two sets and all other omic features via bipartite Pearson correlations.

resultsThis dataset revealed that the set of metabolome and methylome signatures of birthweight have seven signals in common, including three metabolites [PC(34:2), plasmalogen PC(36:4)/PC(O-36:5), and a compound with m/z of 781.0545], two CpGs (on the DHCR24 and SC4MOL gene), and two proteins (periostin and CCL22). CCL22, a macrophage-derived chemokine has not been previously identified in relation to birthweight. Since the results of the omics integration indicated the central role of cholesterol metabolism, we explored the association of cholesterol levels in cord blood with birthweight in the ENVIRONAGE cohort (n = 1097), finding that higher birthweight was associated with increased high-density lipoprotein cholesterol and that high-density lipoprotein cholesterol was lower in small versus large for gestational age newborns.

conclusionsOur data suggests that an integration of different omic-layers in addition to single omics studies is a useful approach to generate new hypotheses regarding biological mechanisms. CCL22 and cholesterol metabolism in cord blood play a mechanistic role in birthweight.

Indexed as

Birth WeightChemokine CCL22CholesterolCross-Sectional StudiesFemaleFetal BloodHumansInfant, NewbornMaleMetabolomeMethylationCCL22 protein, humanChemokine CCL22CholesterolBirth weightCholesterolDNA methylationGene expressionMetabolomeProteins

Identifiers

PMID32553738
PMCPMC7450273
OpenAlexW3034996730

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.