Multiple micronutrient deficiencies alter energy metabolism in host and gut microbiome in an early-life murine model
Bibliographic record
Abstract
Introduction: Micronutrients perform a wide range of physiological functions essential for growth and development. However, most people still need to meet the estimated average requirement worldwide. Globally, 2 billion people suffer from micronutrient deficiency, most of which are co-occurring deficiencies in children under age five. Despite decades of research, animal models studying multiple micronutrient deficiencies within the early-life period are lacking, which hinders our complete understanding of the long-term health implications and may contribute to the inefficacy of some nutritional interventions. Evidence supporting the Developmental Origins of Health and Disease (DOHaD) theory demonstrates that early-life nutritional deficiencies carry life-long consequences mediated through various mechanisms such as abnormal metabolic programming, stunting, altered body composition, and the gut microbiome. However, this is largely unexplored in the multiple micronutrient deficient host. Methods: we developed a preclinical model to examine undernutrition's metabolic and functional impact on the host and gut microbiome early in life. Three-week-old weanling C57BL/6N male mice were fed a low-micronutrient diet deficient in zinc, folate, iron, vitamin A, and vitamin B12 or a control diet for 4-weeks. Results: Our results showed that early-life multiple micronutrient deficiencies induced stunting, altered body composition, impaired glucose and insulin tolerance, and altered the levels of other micronutrients not depleted in the diet within the host. In addition, functional metagenomics profiling and a carbohydrate fermentation assay showed an increased microbial preference for simple sugars rather than complex ones, suggestive of a less developed microbiome in the low-micronutrient-fed mice. Moreover, we found that a zinc-only deficient diet was not sufficient to induce these phenotypes, further supporting the importance of studying co-occurring deficiencies. Discussion: Together, these findings highlight a previously unappreciated role of early-life multiple micronutrient deficiencies in shaping the metabolic phenome of the host and gut microbiome through altered glucose energy metabolism, which may have implications for metabolic disease later in life in micronutrient-deficient survivors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".