Microbes: possible link between modern lifestyle transition and the rise of metabolic syndrome
Bibliographic record
Abstract
The rapid decrease in infectious diseases globally has coincided with an increase in the prevalence of obesity and other components of metabolic syndrome. Insulin resistance is a common feature of metabolic syndrome and can be influenced by genetic and non-genetic/environmental factors. The emergence of metabolic syndrome epidemics over only a few decades suggests a more prominent role of the latter. Changes in our environment and lifestyle have indeed paralleled the rise in metabolic syndrome. Gastrointestinal tract microbiota, the composition of which plays a significant role in host physiology, including metabolism and energy homeostasis, are distinctly different within the context of metabolic syndrome. Among humans, recent lifestyle-related changes could be linked to changes in diversity and composition of 'ancient' microbiota. Given the co-adaptation and co-evolution of microbiota with the immune system over a long period of time, it is plausible that such lifestyle-related microbiota changes could trigger aberrant immune responses, thereby predisposing an individual to a variety of diseases. Here, we review current evidence supporting a role for gut microbiota in the ongoing rise of metabolic syndrome. We conclude that population-level shifts in microbiota can play a mediatory role between lifestyle factors and pathogenesis of insulin resistance and metabolic syndrome.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".