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Record W7161771828 · doi:10.82308/19808

The effects of gestational weight gain on vitamin D status of the neonate

2018· dissertation· en· W7161771828 on OpenAlexaboutno aff
K Mullahoo

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVitamin D and neurologyWeight gainBody mass indexGestationPregnancyVitaminvitamin D deficiency

Abstract

fetched live from OpenAlex

Introduction: Vitamin D deficiency is common in pregnant women and subsequently, their offspring can be born with low status. Growing evidence suggests that vitamin D requirements may be weight dependent, due to its liposolubility, and/or due to volume dilution effects in larger individuals. These mechanisms are applicable to pregnant women, as the largest constituents of gestational weight gain (GWG) are increasing fat stores and fluid accumulations. Associations between GWG and vitamin D status of neonates and their mothers has not been extensively studied.Objectives: Primary: To test for relationships between maternal GWG and vitamin D status of their newborn infant. Secondary: To explore relationships between GWG and maternal vitamin D status, pre-gravid body mass index (BMI) and vitamin D status of the neonate and mother.Methodology: Healthy mother-infant pairs (n=59) from the greater Montreal area were recruited 24-36 hours post-partum (clinicaltrials.gov: NCT02563015). A demographic survey was administered to participants and dietary intake of vitamin D was assessed using a validated food frequency questionnaire (FFQ) to reflect pregnancy. Blood samples were collected from mothers and their infants to assess serum 25-hydroxyvitamin D [25(OH)D] concentrations using a chemiluminescent assay, Liaison auto-analyzer (Diasorin Inc.). Infant and maternal anthropometrics were obtained from medical charts. Data were analyzed in three groups: mothers who had inadequate (n=17), adequate (n=18) or excessive (n=24) GWG as per Health Canada’s recommendations. A mixed model ANOVA and a linear regression were performed (SAS, v9.3), with GWG and infant sex as fixed effects. For the three GWG categories, p<0.05 after adjustment for multiple comparisons using Scheffe’s test was considered statistically significant. Data are mean ±standard deviation unless otherwise noted.Results: Maternal pre-gravid body mass index was 24.7 ± 5.1 kg/m2, infant birth weight-for-age Z-score was 0.2 ± 0.8 and 54% were male. Infant serum 25(OH)D concentration was 40.9 ± 18.9 nmol/L, with 29% <30 nmol/L and 42% <50 nmol/L; status did not differ between sexes. Maternal serum 25(OH)D concentration was 62.7 ± 25.8 nmol/L with 7% <30 nmol/L and 35% <50 nmol/L. Maternal and infant serum 25(OH)D were significantly related (r=0.74, p<0.0001). Mean serum 25(OH)D concentrations were significantly lower between GWG groups for infants born to mothers with excessive compared to adequate GWG (ANOVA p=0.02). Mothers’ concentrations did not differ amongst GWG categories. In regression analysis (R^2=0.57), neonatal serum 25(OH)D concentration was 15.2 nmol/L higher when born to white compared to non-white mothers and 4.2 nmol/L higher for each 10 nmol/L increment in maternal serum 25(OH)D. Significant correlations between pre-gravid BMI and serum 25(OH)D concentration were only found in neonates (r=-0.34, p=0.008; maternal model: r=0.24, p=0.06). Further investigation accounting for covariates using a mixed model ANOVA and linear regression showed no differences between BMI categories for mothers or infants.Conclusion: These results suggest that mothers with excessive GWG are more likely to have infants born with vitamin D insufficiency, independent of being non-white or having lower vitamin D status. Additionally, a large proportion of infants (71%) were vitamin D insufficient at birth despite the fact that more than 60% of our mothers met or exceeded the RDA of 600 IU of vitamin D daily during pregnancy. Further research on the role of GWG on vitamin D status during pregnancy, as well as a revision of dietary recommendations for this population, are warranted.

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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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.307
Teacher spread0.299 · 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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Citations0
Published2018
Admission routes1
Has abstractyes

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