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Record W3195896170 · doi:10.1096/fj.202100198rr

Paternal high protein diet modulates body composition, insulin sensitivity, epigenetics, and gut microbiota intergenerationally in rats

2021· article· en· W3195896170 on OpenAlexafffund
Faye Chleilat, Alana Schick, Julie M. Deleemans, Kyle Ma, Erna Alukic, Jolene Wong, Erin W. Noye Tuplin, Jodi E. Nettleton, Raylene A. Reimer

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversity of Calgary
FundersAlberta Children's Hospital Research Institute
KeywordsInsulin sensitivityBiologyGut floraEpigeneticsInsulinEndocrinologyComposition (language)Internal medicineInsulin resistanceGeneticsBiochemistryMedicineGene

Abstract

fetched live from OpenAlex

Mounting evidence demonstrates that paternal diet programs offspring metabolism. However, the contribution of a pre-conception paternal high protein (HP) diet to offspring metabolism, gut microbiota, and epigenetic changes remains unclear. Here we show that paternal HP intake in Sprague Dawley rats programs protective metabolic outcomes in offspring. Compared to paternal high fat/sucrose (HF/S), HP diet improved body composition and insulin sensitivity and improved circulating satiety hormones and cecal short-chain fatty acids compared to HF/S and control diet (P < .05). Further, using 16S rRNA gene sequencing to assess gut microbial composition, we observed increased alpha diversity, distinct bacterial clustering, and increased abundance of Bifidobacterium, Akkermansia, Bacteroides, and Marvinbryantia in HP fathers and/or male and female adult offspring. At the epigenetic level, DNMT1and 3b expression was altered intergenerationally. Our study identifies paternal HP diet as a modulator of gut microbial composition, epigenetic markers, and metabolic function intergenerationally.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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

Citations30
Published2021
Admission routes2
Has abstractyes

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Same venueThe FASEB JournalSame topicBirth, Development, and HealthFrench-language works237,207