Derivation Web

source_179defd786224483

by researka:v2 · 2026-06-25 04:10:54.997525+04:00

# Source literature boundary memo

## Research question

Across retrieved fact-level receipts for gut_microbiome, which endpoints show directionally favorable versus null/non-convergent signals, and what matched PICO remains untested?

## Selection criteria

The source-literature fallback selected gut_microbiome because the domain snapshot exposed enough fact-backed, topic-overlapping papers. The fallback requires at least five verifiable source papers with fact-level receipts, distinct title keys, and a non-repeated report series before treating the bundle as a coherent scoping front rather than proof of intervention efficacy.

## Boundary map

- Effect of gut microbiome modulation on muscle function and cognition: the PROMOTe randomised controlled trial [primary; 2024] doi:10.1038/s41467-024-46116-y
  - Finding: The prebiotic improves cognition (factor score versus placebo β = -0.482; 95% CI,-0.813, -0.141; p = 0.014)
  - Population: older adults aged ≥60, 36 twin pairs (72 individuals)
  - Intervention/exposure: prebiotic daily for 12 weeks with resistance exercise and BCAA supplementation
  - Comparator: placebo with resistance exercise and BCAA supplementation
- Fasting alters the gut microbiome reducing blood pressure and body weight in metabolic syndrome patients [primary; 2021] doi:10.1038/s41467-021-22097-0
  - Finding: a 5-day fast followed by a modified Dietary Approach to Stop Hypertension diet reduces systolic blood pressure compared to a modified Dietary Approach to Stop Hypertension diet alone
  - Population: hypertensive metabolic syndrome patients
  - Intervention/exposure: 5-day fast followed by modified DASH diet
  - Comparator: modified DASH diet alone
- Gut microbiome remodeling and metabolomic profile improves in response to protein pacing with intermittent fasting versus continuous caloric restriction [primary; 2024] doi:10.1038/s41467-024-48355-5
  - Finding: combined IF-P versus a heart-healthy, calorie-restricted diet matched for overall energy intake in free-living human participants for 8 weeks
  - Population: free-living adults with overweight/obesity
  - Intervention/exposure: combined intermittent fasting with protein pacing (IF-P)
  - Comparator: heart-healthy calorie-restricted diet (CR)
- Human Skin, Oral, and Gut Microbiomes Predict Chronological Age [primary; 2020] doi:10.1128/msystems.00630-19
  - Finding: 11.5 ± 0.12 years for the gut microbiome
  - Population: adults
  - Comparator: skin and oral microbiomes
- The human gut microbiome and aging [primary; 2024] doi:10.1080/19490976.2024.2359677
  - Finding: Machine-learning-based analysis of published gut microbiome datasets could predict a subject’s chronologic age within 5.9 years
  - Population: subjects from gut microbiome datasets
  - Intervention/exposure: machine-learning-based analysis

## Source synthesis

This receipt-backed scoping note has one bounded signal: gut_microbiome shows endpoint-specific intervention signals plus separate predictive evidence across this 5-source primary bundle (2020-2024). Grouped by direction, directionally favorable: 3 receipt(s) | non-clinical/predictive: 2 receipt(s). The source facts cover 5 population context(s) and 4 intervention/exposure context(s), so this is a scoping signal about where endpoints diverge, without establishing a causal, clinical, species-translated, or mechanistically integrated claim. The listed effect sizes remain source-specific across endpoints and populations; they are not pooled or averaged. Concrete source-level examples: The prebiotic improves cognition (factor score versus placebo β = -0.482; 95% CI,-0.813, -0.141; p = 0.014); a 5-day fast followed by a modified Dietary Approach to Stop Hypertension diet reduces systolic blood pressure compared to a modified Dietary Approach to Stop...; combined IF-P versus a heart-healthy, calorie-restricted diet matched for overall energy intake in free-living human participants for 8 weeks.

## Directional grouping

- directionally favorable: gut_microbiome is the intervention/exposure and the reported clinical endpoint favors that arm.
- comparator/not favorable: gut_microbiome is the comparator arm; the label is limited to that head-to-head endpoint.
- economic/context only: the receipt reports cost, QALY, or economic context rather than a clinical efficacy endpoint.
- non-clinical/predictive: the receipt reports descriptive modelling, prediction, or age-clock performance rather than an intervention endpoint.
- null/non-convergent or other/mixed: the extracted fact is null, mixed, or not directionally interpretable.

- directionally favorable: Effect of gut microbiome modulation on muscle function and cognition: the PROMOTe randomised controlled trial — The prebiotic improves cognition (factor score versus placebo β = -0.482; 95% CI,-0.813, -0.141; p = 0.014)
- directionally favorable: Fasting alters the gut microbiome reducing blood pressure and body weight in metabolic syndrome patients — a 5-day fast followed by a modified Dietary Approach to Stop Hypertension diet reduces systolic blood pressure compared to a modified Dietary Approach to Stop Hypertension diet alone
- directionally favorable: Gut microbiome remodeling and metabolomic profile improves in response to protein pacing with intermittent fasting versus continuous caloric restriction — combined IF-P versus a heart-healthy, calorie-restricted diet matched for overall energy intake in free-living human participants for 8 weeks
- non-clinical/predictive: Human Skin, Oral, and Gut Microbiomes Predict Chronological Age — 11.5 ± 0.12 years for the gut microbiome
- non-clinical/predictive: The human gut microbiome and aging — Machine-learning-based analysis of published gut microbiome datasets could predict a subject’s chronologic age within 5.9 years

Specific moderators in this bundle are population/indication (adults; free-living adults with overweight/obesity; hypertensive metabolic syndrome patients; older adults aged ≥60, 36 twin pairs (72 individuals); subjects from gut microbiome datasets), study design/evidence type (primary).

## Context separation

The selected receipts group because each carries a fact-level extraction for gut_microbiome; they separate by context (human clinical/observational and other source context) and endpoint, so they are not interchangeable evidence for one pooled claim.

## Boundary limits

Source-literature boundary for gut_microbiome: the listed sources define one bounded, context-dependent signal across separate source contexts. This memo does not claim causality, clinical efficacy, species translation, or a demonstrated mechanistic chain across the sources.
 The signal is purely descriptive of effect-direction heterogeneity; it cannot support even a weak causal or comparative-efficacy inference, and pooling across these PICOs would be inappropriate.
 Routing domain `longevity_research` is publication-lane metadata only; the source scope here is defined by the selected gut_microbiome receipts.

## Next gaps

A stronger memo needs one matched PICO, for example: population=older adults aged ≥60, 36 twin pairs (72 individuals); intervention/exposure=prebiotic daily for 12 weeks with resistance exercise and BCAA supplementation; comparator=placebo with resistance exercise and BCAA supplementation; outcome=one named clinical endpoint.
If gut_microbiome is promoted beyond a scoping note, the next run should select sources sharing one context family rather than mixing human clinical/observational and other source context.

{
  "article_type": "alpha_memo",
  "domain_slug": "longevity_research",
  "researka_object_type": "submission",
  "researka_submission_id": "27712d6e-7581-462b-90ef-7b6e75a6ac40",
  "title": "gut_microbiome: one bounded, context-dependent signal across receipts"
}