Evidence mapPaperPMID 34043632Full record

SynthesisPloS one2021

Metabolomic profiling identifies complex lipid species and amino acid analogues associated with response to weight loss interventions.

Nathan A Bihlmeyer, Lydia Coulter Kwee, Clary B Clish, Amy Anderson Deik, Robert E Gerszten, Neha J Pagidipati, Blandine Laferrère, Laura P Svetkey, Christopher B Newgard, William E Kraus and 1 more

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

13 citing papers in PubMed, 19 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

11 authors at 4 institutions in 1 country.

Nathan A BihlmeyerDuke Molecular Physiology Institute, Duke University, Durham, North Carolina, United States of America.ORCID 0000-0002-4415-7419
Lydia Coulter KweeDuke Molecular Physiology Institute, Duke University, Durham, North Carolina, United States of America.
Clary B ClishMetabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.ORCID 0000-0001-8259-9245
Amy Anderson DeikMetabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.ORCID 0000-0002-9687-0953
Robert E GersztenDivision of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America.
Neha J PagidipatiDuke Clinical Research Institute, Duke University, Durham, North Carolina, United States of America.
Blandine LaferrèreColumbia University Irving Medical Center, New York, New York, United States of America.
Laura P SvetkeyDepartment of Medicine, Duke University School of Medicine, Durham, North Carolina, United States of America.
Christopher B NewgardDuke Molecular Physiology Institute, Duke University, Durham, North Carolina, United States of America.
William E KrausDuke Molecular Physiology Institute, Duke University, Durham, North Carolina, United States of America.
Svati H ShahDuke Molecular Physiology Institute, Duke University, Durham, North Carolina, United States of America.
Duke University · USBroad Institute · USBeth Israel Deaconess Medical Center · USColumbia University Irving Medical Center · US

Funding

Metabolomic predictors of insulin resistance and diabetesR01DK081572 · BETH ISRAEL DEACONESS MEDICAL CENTER · 2025 to 2025
$649k
NHLBI NIH HHS R01 HL127009NIDDK NIH HHS R01 DK081572
6 · The paper itself

Abstract

Obesity is an epidemic internationally. While weight loss interventions are efficacious, they are compounded by heterogeneity with regards to clinically relevant metabolic responses. Thus, we sought to identify metabolic biomarkers that are associated with beneficial metabolic changes to weight loss and which distinguish individuals with obesity who would most benefit from a given type of intervention. Liquid chromatography mass spectrometry-based profiling was used to measure 765 metabolites in baseline plasma from three different weight loss studies: WLM (behavioral intervention, N = 443), STRRIDE-PD (exercise intervention, N = 163), and CBD (surgical cohort, N = 125). The primary outcome was percent change in insulin resistance (as measured by the Homeostatic Model Assessment of Insulin Resistance [%ΔHOMA-IR]) over the intervention. Overall, 92 individual metabolites were associated with %ΔHOMA-IR after adjustment for multiple comparisons. Concordantly, the most significant metabolites were triacylglycerols (TAGs; p = 2.3e-5) and diacylglycerols (DAGs; p = 1.6e-4), with higher baseline TAG and DAG levels associated with a greater improvement in insulin resistance with weight loss. In tests of heterogeneity, 50 metabolites changed differently between weight loss interventions; we found amino acids, peptides, and their analogues to be most significant (4.7e-3) in this category. Our results highlight novel metabolic pathways associated with heterogeneity in response to weight loss interventions, and related biomarkers which could be used in future studies of personalized approaches to weight loss interventions.

Indexed as

MetabolomicsAdultBiomarkersBody Mass IndexDiglyceridesFemaleHumansInsulin ResistanceLipid MetabolismLipidsMaleMass SpectrometryMiddle AgedObesityTriglyceridesWeight LossBiomarkersDiglyceridesLipidsTriglycerides

Identifiers

PMID34043632
PMCPMC8158886
OpenAlexW3165457241

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.