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Record W4224209993 · doi:10.3390/ijms23084467

Recent Experimental Studies of Maternal Obesity, Diabetes during Pregnancy and the Developmental Origins of Cardiovascular Disease

2022· review· en· W4224209993 on OpenAlexafffund
Stephanie M. Kereliuk, Vernon W. Dolinsky

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2022
Typereview
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersManitoba Medical Service FoundationResearch Manitoba
KeywordsOffspringObesityPregnancyDiseaseMedicineDiabetes mellitusGestational diabetesHeart diseaseInternal medicineEndocrinologyGestationBiology

Abstract

fetched live from OpenAlex

Globally, cardiovascular disease remains the leading cause of death. Most concerning is the rise in cardiovascular risk factors including obesity, diabetes and hypertension among youth, which increases the likelihood of the development of earlier and more severe cardiovascular disease. While lifestyle factors are involved in these trends, an increasing body of evidence implicates environmental exposures in early life on health outcomes in adulthood. Maternal obesity and diabetes during pregnancy, which have increased dramatically in recent years, also have profound effects on fetal growth and development. Mounting evidence is emerging that maternal obesity and diabetes during pregnancy have lifelong effects on cardiovascular risk factors and heart disease development. However, the mechanisms responsible for these observations are unknown. In this review, we summarize the findings of recent experimental studies, showing that maternal obesity and diabetes during pregnancy affect energy metabolism and heart disease development in the offspring, with a focus on the mechanisms involved. We also evaluate early proof-of-concept studies for interventions that could mitigate maternal obesity and gestational diabetes-induced cardiovascular disease risk in the offspring.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.068
GPT teacher head0.365
Teacher spread0.298 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations34
Published2022
Admission routes2
Has abstractyes

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