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Record W3036711917 · doi:10.1161/circresaha.120.316653

Transient Intermittent Hyperglycemia Accelerates Atherosclerosis by Promoting Myelopoiesis

2020· article· en· W3036711917 on OpenAlexfundno aff
Michelle Flynn, Michael J. Kraakman, Christos Tikellis, Man K.S. Lee, Nordin M.J. Hanssen, Hélène L. Kammoun, R. J. Pickering, Dragana Dragoljevic, Annas Al‐Sharea, Tessa J. Barrett, Fiona Hortle, Frances L. Byrne, Ellen M. Olzomer, Domenica A. McCarthy, Casper G. Schalkwijk, Josephine M. Forbes, Kyle L. Hoehn, Liza Makowski, Graeme I. Lancaster, Assam El‐Osta, Edward A. Fisher, Ira J. Goldberg, Mark E. Cooper, Prabhakara R. Nagareddy, Merlin C. Thomas, Andrew Murphy

Bibliographic record

VenueCirculation Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Glycation End Products research
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteBaker Heart and Diabetes InstituteNational Health and Medical Research CouncilMedical Research CouncilCSL BehringHealth Science Center, University of TennesseeNational Heart Foundation of AustraliaSchool of Medicine, New York UniversityDiabetes FondsUniversiteit MaastrichtCARIM School for Cardiovascular Diseases, Universiteit MaastrichtYork UniversityHartstichtingEuropean Foundation for the Study of DiabetesUniversity of New South WalesMater FoundationDiabetes Australia Research TrustMonash UniversityDiabetes AustraliaNational Institutes of HealthChinese University of Hong KongOhio State UniversityUniversity of QueenslandAmerican Heart Association
KeywordsMyelopoiesisInternal medicineMonocytosisEndocrinologyDiabetes mellitusMedicineMyeloidPrediabetesBone marrowBiologyType 2 diabetesHaematopoiesis

Abstract

fetched live from OpenAlex

Rationale: Treatment efficacy for diabetes mellitus is largely determined by assessment of HbA1c (glycated hemoglobin A1c) levels, which poorly reflects direct glucose variation. People with prediabetes and diabetes mellitus spend >50% of their time outside the optimal glucose range. These glucose variations, termed transient intermittent hyperglycemia (TIH), appear to be an independent risk factor for cardiovascular disease, but the pathological basis for this association is unclear. Objective: To determine whether TIH per se promotes myelopoiesis to produce more monocytes and consequently adversely affects atherosclerosis. Methods and Results: To create a mouse model of TIH, we administered 4 bolus doses of glucose at 2-hour intervals intraperitoneally once to WT (wild type) or once weekly to atherosclerotic prone mice. TIH accelerated atherogenesis without an increase in plasma cholesterol, seen in traditional models of diabetes mellitus. TIH promoted myelopoiesis in the bone marrow, resulting in increased circulating monocytes, particularly the inflammatory Ly6-C hi subset, and neutrophils. Hematopoietic-restricted deletion of S100a9 , S100a8 , or its cognate receptor Rage prevented monocytosis. Mechanistically, glucose uptake via GLUT (glucose transporter)-1 and enhanced glycolysis in neutrophils promoted the production of S100A8/A9. Myeloid-restricted deletion of Slc2a1 (GLUT-1) or pharmacological inhibition of S100A8/A9 reduced TIH-induced myelopoiesis and atherosclerosis. Conclusions: Together, these data provide a mechanism as to how TIH, prevalent in people with impaired glucose metabolism, contributes to cardiovascular disease. These findings provide a rationale for continual glucose control in these patients and may also suggest that strategies aimed at targeting the S100A8/A9-RAGE (receptor for advanced glycation end products) axis could represent a viable approach to protect the vulnerable blood vessels in diabetes mellitus. Graphic Abstract: A graphic abstract is available for this article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.094
GPT teacher head0.359
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations166
Published2020
Admission routes1
Has abstractyes

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