Evidence map›Paper›PMID 36252453›Full record

ArticleMolecular genetics and metabolism2022

Metabolic diversity in human populations and correlation with genetic and ancestral geographic distances.

Gang Peng, Andrew J Pakstis, Neeru Gandotra, Tina M Cowan, Hongyu Zhao, Kenneth K Kidd, Curt Scharfe

Open access · hybridAbstract read
In one paragraph

Article in Molecular genetics and metabolism, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

11 citing papers in PubMed, 15 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

7 authors at 2 institutions in 1 country.

Gang PengDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA; Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA.
Andrew J PakstisDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA.
Neeru GandotraDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA.
Tina M CowanDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Hongyu ZhaoDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA; Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA.
Kenneth K KiddDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA.
Curt ScharfeDepartment of Genetics, Yale University School of Medicine, New Haven, CT, USA. Electronic address: curt.scharfe@yale.edu.
Yale University · USStanford University · US

Funding

Multiplex gene sequencing and metabolomics analysis from newborn dried blood spots to improve screening and diagnosis of metabolic disorders.R01HD102537 · NICHD · YALE UNIVERSITY · PI SCHARFE, CURT · 2020 to 2024
$3.0M
NICHD NIH HHS R01 HD102537
6 · The paper itself

Abstract

DNA polymorphic markers and self-defined ethnicity groupings are used to group individuals with shared ancient geographic ancestry. Here we studied whether ancestral relationships between individuals could be identified from metabolic screening data reported by the California newborn screening (NBS) program. NBS data includes 41 blood metabolites measured by tandem mass spectrometry from singleton babies in 17 parent-reported ethnicity groupings. Ethnicity-associated differences identified for 71% of NBS metabolites (29 of 41, Cohen's d > 0.5) showed larger differences in blood levels of acylcarnitines than of amino acids (P < 1e-4). A metabolic distance measure, developed to compare ethnic groupings based on metabolic differences, showed low positive correlation with genetic and ancient geographic distances between the groups' ancestral world populations. Several outlier group pairs were identified with larger genetic and smaller metabolic distances (Black versus White) or with smaller genetic and larger metabolic distances (Chinese versus Japanese) indicating the influence of genetic and of environmental factors on metabolism. Using machine learning, comparison of metabolic profiles between all pairs of ethnic groupings distinguished individuals with larger genetic distance (Black versus Chinese, AUC = 0.96), while genetically more similar individuals could not be separated metabolically (Hispanic versus Native American, AUC = 0.51). Additionally, we identified metabolites informative for inferring metabolic ancestry in individuals from genetically similar populations, which included biomarkers for inborn metabolic disorders (C10:1, C12:1, C3, C5OH, Leucine-Isoleucine). This work sheds new light on metabolic differences in healthy newborns in diverse populations, which could have implications for improving genetic disease screening.

Indexed as

Metabolism, Inborn ErrorsAmino AcidsBiomarkersHumansInfant, NewbornNeonatal ScreeningTandem Mass SpectrometryAmino AcidsBiomarkersBiochemical geneticsInborn metabolic disordersMetabolismNewborn screeningPopulation geneticsPublic health

Identifiers

PMID36252453
PMCPMC10131177
OpenAlexW4304890249

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.