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Record W4397018323 · doi:10.1101/2024.05.15.24307442

Do nutritional interventions before or during pregnancy affect placental phenotype? Findings from a systematic review of human clinical trials

2024· review· en· W4397018323 on OpenAlexafffund
Victoria Bonnell, Marina White, KL Connor

Bibliographic record

VenuemedRxiv · 2024
Typereview
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsAffect (linguistics)Psychological interventionPregnancyPhenotypeClinical trialMedicineSystematic reviewObstetricsBioinformaticsBiologyMEDLINEPsychologyPsychiatryGeneticsBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background Maternal nutritional interventions aim to address nutrient deficiencies in pregnancy, a leading cause of maternal and neonatal morbidity and mortality worldwide. How these interventions influence the placenta, which plays a vital role in fetal growth and nutrient supply, is not well understood. This is a major gap in understanding how such interventions could influence pregnancy outcomes and fetal health. We hypothesised that nutritional interventions influence placental phenotype, and that these placental changes relate to how successful, or not, the intervention is in improving pregnancy outcomes. Methods We conducted a systematic review and followed PRISMA-2020 reporting guidelines. Articles were retrieved from PubMed, Clinicaltrials.gov, and ICTRP-WHO using pre-defined search terms and screened by two reviewers using a 3-level process. Inclusion criteria considered articles published from January 2001-September 2021 that reported on clinical trials in humans, which administered a maternal nutritional intervention during the periconceptional or pregnancy period and reported on placental phenotype (shape and form, function or placental disorders). Findings Fifty-three eligible articles reported on (multiple) micronutrient- (n=33 studies), lipid- (n=11), protein- (n=2), and diet-/lifestyle-based (n=8) interventions. Of the micronutrient-based interventions, 16 (48%) associated with altered placental function, namely altered nutrient transport/metabolism (n=9). Nine (82%) of the lipid-based interventions associated with altered placental phenotype, including elevated placental fatty acid levels (n=5), altered nutrient transport/metabolism gene expression (n=4), and decreased inflammatory biomarkers (n=2). Of the protein-based interventions, two (66%) associated with altered placental phenotype, including increased placental efficiency (n=1) or decreased preeclampsia risk (n=1). Three (38%) of diet and lifestyle-based interventions associated with placental changes, namely placental gene expression (n=1) and disease (n=2). In studies with data on maternal (n=30) or offspring (n=20) outcomes, interventions that influenced placental phenotype were more likely to have also associated with improved maternal outcomes (11/15 [73%]) and offspring birth outcomes (6/11 [54%]), compared to interventions that did not associate with placental changes (2/15 [13%] and 1/9 [11%], respectively). Conclusions Periconceptional and prenatal nutritional interventions to improve maternal/pregnancy health associate with altered placental development and function. These placental adaptations likely benefit the pregnancy and improve offspring outcomes. Understanding the placenta’s role in the success of interventions to combat nutrient deficiencies is critical for improving interventions and reducing maternal and neonatal morbidity and mortality globally.

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)0.050
metaresearch head score (Gemma)0.239
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.050
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.239
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0160.015
Bibliometrics0.0120.015
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.274
GPT teacher head0.512
Teacher spread0.237 · 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 designSystematic review
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

Citations0
Published2024
Admission routes2
Has abstractyes

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