Reduce, reinforce, and replenish: safeguarding the early-life microbiota to reduce intergenerational health disparities
Bibliographic record
Abstract
Socioeconomic (SE) disparity and health inequity are closely intertwined and associated with cross-generational increases in the rates of multiple chronic non-communicable diseases (NCDs) in North America and beyond. Coinciding with this social trend is an observed loss of biodiversity within the community of colonizing microbes that live in and on our bodies. Researchers have rightfully pointed to the microbiota as a key modifiable factor with the potential to ease existing health inequities. Although a number of studies have connected the adult microbiome to socioeconomic determinants and health outcomes, few studies have investigated the role of the infant microbiome in perpetuating these outcomes across generations. It is an essential and important question as the infant microbiota is highly sensitive to external forces, and observed shifts during this critical window often portend long-term outcomes of health and disease. While this is often studied in the context of direct modulators, such as delivery mode, family size, antibiotic exposure, and breastfeeding, many of these factors are tied to underlying socioeconomic and/or cross-generational factors. Exploring cross-generational socioeconomic and health inequities through the lens of the infant microbiome may provide valuable avenues to break these intergenerational cycles. In this review, we will focus on the impact of social inequality in infant microbiome development and discuss the benefits of prioritizing and restoring early-life microbiota maturation for reducing intergenerational health disparities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".