Evidence map›Paper›PMID 37034622›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Gut microbial features and dietary fiber intake predict gut microbiota response to resistant starch supplementation.

Sri Lakshmi Sravani Devarakonda, Dorothy K Superdock, Jennifer Ren, Lynn M Johnson, Aura Alex P Loinard-Gonz Lez, Angela C Poole

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Sri Lakshmi Sravani DevarakondaORCID 0000-0001-8758-7227
Dorothy K SuperdockORCID 0000-0001-5348-3307
Jennifer Ren
Lynn M Johnson
Aura Alex P Loinard-Gonz Lez
Cornell University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Resistant starch (RS) consumption can have beneficial effects on human health, but the response, in terms of effects on the gut microbiota and host physiology, varies between individuals. Factors predicting the response to RS are not yet established and would be useful for developing precision nutrition approaches that maximize the benefits of dietary fiber intake. We sought to identify predictors of gut microbiota response to RS supplementation. We enrolled 76 healthy adults into a seven-week crossover study. Participants consumed RS type 2 (RS2), RS type 4 (RS4), and a digestible starch, for ten days each with five-day washout periods in between. We collected fecal and saliva samples and food records before and during each treatment period. We performed 16S rRNA gene sequencing and measured fecal short-chain fatty acids (SCFAs), salivary amylase gene copy number, and salivary amylase activity (SAA). Dietary fiber intake was predictive of relative abundance of several amplicon sequence variants (ASVs) at the end of both RS treatments. Treatment order (the order of consumption of RS2 and RS4), alpha diversity, and a subset of ASVs were predictive of SCFA changes after RS supplementation. SAA was only predictive of the relative abundance of ASVs after digestible starch supplementation. Based on our findings, dietary fiber intake and gut microbiome composition would be informative if assessed prior to recommending RS supplementation. Using a precision nutrition approach to optimize the benefits of dietary fibers such as RS could be an effective strategy to compensate for the low consumption of dietary fiber nationwide.

Identifiers

PMID37034622
PMCPMC10081390
OpenAlexW4361294663

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.