Evidence map›Paper›PMID 36984857›Full record

ReviewMetabolites2023

The Impact of Nutrient Intake and Metabolic Wastes during Pregnancy on Offspring Hypertension: Challenges and Future Opportunities.

You-Lin Tain, Chien-Ning Hsu

Abstract readReview
In one paragraph

Review in Metabolites, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

You-Lin TainDivision of Pediatric Nephrology, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan.ORCID 0000-0002-7059-6407
Chien-Ning HsuDepartment of Pharmacy, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan.ORCID 0000-0001-7470-528X

Funding

Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan CMRPG8M1371, CMRPG8N0171, CMRPG8M0721 and CMRPG8M0751
6 · The paper itself

Abstract

Hypertension can have its origin in early life. During pregnancy, many metabolic alterations occur in the mother that have a crucial role in fetal development. In response to maternal insults, fetal programming may occur after metabolic disturbance, resulting in programmed hypertension later in life. Maternal dietary nutrients act as metabolic substrates for various metabolic processes via nutrient-sensing signals. Different nutrient-sensing pathways that detect levels of sugars, amino acids, lipids and energy are integrated during pregnancy, while disturbed nutrient-sensing signals have a role in the developmental programming of hypertension. Metabolism-modulated metabolites and nutrient-sensing signals are promising targets for new drug discovery due to their pathogenic link to hypertension programming. Hence, in this review, we pay particular attention to the maternal nutritional insults and metabolic wastes affecting fetal programming. We then discuss the role of nutrient-sensing signals linking the disturbed metabolism to hypertension programming. This review also summarizes current evidence to give directions for future studies regarding how to prevent hypertension via reprogramming strategies, such as nutritional intervention, targeting nutrient-sensing signals, and reduction of metabolic wastes. Better prevention for hypertension may be possible with the help of novel early-life interventions that target altered metabolism.

Indexed as

AMP-activated protein kinase (AMPK)asymmetric dimethylargininedevelopmental origins of health and disease (DOHaD)hypertensionnutrient-sensing signalshort chain fatty acidtrimethylamine-N-oxideuremic toxin

Identifiers

PMID36984857
PMCPMC10052993

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.