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Record W3124427543 · doi:10.1186/s12916-021-01910-z

When a pandemic and an epidemic collide: COVID-19, gut microbiota, and the double burden of malnutrition

2021· article· en· W3124427543 on OpenAlexafffund
Paula Littlejohn, B. Brett Finlay

Bibliographic record

VenueBMC Medicine · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCanadian Institutes of Health ResearchCanadian Institute for Advanced Research
KeywordsOvernutritionMalnutritionMedicineMicrobiomePandemicEnvironmental healthGut floraDysbiosisObesityDouble burdenDiseaseOverweightImmunologyCoronavirus disease 2019 (COVID-19)BiologyInfectious disease (medical specialty)Bioinformatics

Abstract

fetched live from OpenAlex

BACKGROUND: It is estimated that the COVID-19 pandemic will drastically increase all forms of malnutrition. Of particular concern, yet understated, is the potential to increase the double burden of malnutrition (DBM) epidemic. This coexistence of undernutrition together with overweight and obesity, or diet-related non-communicable disease (NCD), within low- to middle-income countries (LMICs) is increasing rapidly. Although multiple factors contribute to the DBM, food insecurity (FI) and gut microbiota dysbiosis play a crucial role. Both under- and overnutrition have been shown to be a consequence of food insecurity. The gut microbiota has also been recently implicated in playing a role in under- and overnutrition, with altered community structure and function common to both. The pandemic has already caused significant shifts in food availability which has immediate effects on the gut microbiome. In this opinion paper, we discuss how COVID-19 may indirectly exacerbate the DBM through food insecurity and the gut microbiome. MAIN TEXT: The World Food Programme (WFP) estimates that 265 million people in LMICs will experience acute hunger in 2020 due to the pandemic, nearly doubling the original projection of 135 million. Global border closures to food trade, loss of food production, and stark decline in household income will exacerbate starvation while simultaneously necessitating that families resort to calorie-dense, nutrient-poor foods, thereby increasing obesity. While food insecurity, which is the persistent lack of consistent access to adequate and nutrient-rich foods, will primarily drive nutrition behavior, the gut microbiome is perhaps a key biological mechanism. Numerous human and animal studies describe low diversity and an increase in inflammatory species as characteristic features of the undernourished and overnourished gut microbiota. Indeed, fecal transplant studies show that microbiota transfer from undernourished and overnourished humans to germ-free mice lacking a microbiome transfers the physical and metabolic phenotype, suggesting a causal role for the microbiota in under- and overnutrition. The observed microbiome dysbiosis within severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) coupled with the DBM presents a viscous cycle. CONCLUSION: Low- to mid-income countries will likely see an increase in the DBM epidemic. Providing access to nutritious foods and protecting individuals' gut microbiome to "flatten the curve" of the DBM trajectory should be prioritized.

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)0.002
metaresearch head score (Gemma)0.007
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0180.004

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 head0.044
GPT teacher head0.344
Teacher spread0.300 · 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".

Quick stats

Citations51
Published2021
Admission routes2
Has abstractyes

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