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The Role of Insulin‐Like Growth Factor‐1 in Programming of Offspring Adiposity by Maternal Folate/ Vitamin B12 Imbalance

2016· article· en· W4389008983 on OpenAlexaffabout
Amanda M. Henderson, Rika E. Aleliunas, Daven C. Tai, Nolan G. J. Chem, Tim Green, Angela M. Devlin

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVitamin B12OffspringPregnancyEndocrinologyInsulin resistanceInternal medicinePopulationMedicineInsulinPhysiologyBiologyEnvironmental healthGenetics

Abstract

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Introduction Developmental programming suggest that prenatal and early postnatal environment, such as maternal nutrition, can impact risk for chronic disease later in life. Recent population studies have reported greater insulin resistance and adiposity in children from mothers with adequate folate but low vitamin B12 (B12) status during pregnancy. Grain products in North America are fortified with folic acid to reduce the incidence of neural tube defects. Folate is required for methyl metabolism and is metabolically linked to vitamin B12. Low B12 status, even when folate is adequate, can trap folate in a metabolically inactive form. Folate deficiency is rare in Canada, yet approximately 1 in 20 Canadians are deficient in B12. The mechanisms underlying the relationship between maternal imbalance of folate/ B12 status and offspring adiposity and insulin resistance is not understood. A study recently reported that maternal B12 deficiency during pregnancy disrupts growth hormone (GH)/insulin‐like growth factor‐1 (IGF‐1) axis, resulting in growth retardation and bone malformation in the offspring. The objective of this study is to determine if programming of offspring adiposity by maternal folate/ B12 imbalance involves disturbances in the GH/IGF‐1 axis. Methods Female mice (C57BL/6J) were fed one of 3 diets: control (2mg folic acid/kg diet, M‐CON), supplemental FA (10mg/kg diet) with adequate B12 (SFA+B12), or supplemental FA without B12 (SFA‐B12). Dams were fed the diets 6 weeks prior to conception, through breeding, pregnancy, and lactation. One male and one female pup from each dam were weaned onto a control diet or a high‐fat (45% energy) diet. Tissue was harvested from offspring mice at 20 weeks post‐weaning. Serum IGF‐1 concentrations were quantified by ELISA. Hepatic Igf1 mRNA and Igfbp2 mRNA expression was quantified by Real‐Time PCR using the ΔΔ Ct method of relative quantification. The effect of maternal diet was determined by 1‐way ANOVA, separately in offspring fed the post weaning control diet and western diet. Results Female SFA‐B12 offspring fed the post weaning control diet had lower (p<0.05) serum IGF‐1 concentrations than M‐CON offspring and SFA+B12 offspring. Female SFA‐B12 offspring fed the post weaning western diet had lower (p=0.08) serum IGF‐1 concentrations than SFA+B12 offspring. No effect of maternal diet on serum IGF‐1 concentrations was observed in male offspring. Female SFA+B12 offspring fed the post weaning western diet had higher (p=0.008) hepatic Igf1 mRNA than M‐CON offspring. No effect of maternal diet on hepatic Igf1 mRNA was observed in male offspring. Male SFA‐B12 offspring fed the post weaning control diet had higher (p=0.028) hepatic Igfbp2 mRNA than M‐CON offspring. No effect of maternal diet on hepatic Igfbp2 mRNA was observed in female offspring. Conclusion These findings suggest a role for IGF‐1 in programming of offspring adiposity by maternal folate/ B12 imbalance. It is vital to understand the implications of folate/B12 imbalances, particularly during pregnancy. Support or Funding Information This work was supported by the NSERC Team Discovery Grant. Amanda M. Henderson is supported by the NSERC Canada Graduate Scholarships‐ Master's Program.

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: Observational · Consensus signal: none
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 head0.010
GPT teacher head0.256
Teacher spread0.245 · 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 designObservational
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".

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Citations1
Published2016
Admission routes2
Has abstractyes

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