When a pandemic and an epidemic collide: COVID-19, gut microbiota, and the double burden of malnutrition
Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".