Intake of <i>Lactobacillus reuteri</i> Improves Incretin and Insulin Secretion in Glucose-Tolerant Humans: A Proof of Concept
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Bibliographic record
Abstract
OBJECTIVE: Ingestion of probiotics can modify gut microbiota and alter insulin resistance and diabetes development in rodents. We hypothesized that daily intake of Lactobacillus reuteri increases insulin sensitivity by changing cytokine release and insulin secretion via modulation of the release of glucagon-like peptides (GLP)-1 and -2. RESEARCH DESIGN AND METHODS: A prospective, double-blind, randomized trial was performed in 21 glucose-tolerant humans (11 lean: age 49 ± 7 years, BMI 23.6 ± 1.7 kg/m(2); 10 obese: age 51 ± 7 years, BMI 35.5 ± 4.9 kg/m(2)). Participants ingested 10(10) b.i.d. L. reuteri SD5865 or placebo over 4 weeks. Oral glucose tolerance and isoglycemic glucose infusion tests were used to assess incretin effect and GLP-1 and GLP-2 secretion, and euglycemic-hyperinsulinemic clamps with [6,6-(2)H2]glucose were used to measure peripheral insulin sensitivity and endogenous glucose production. Muscle and hepatic lipid contents were assessed by (1)H-magnetic resonance spectroscopy, and immune status, cytokines, and endotoxin were measured with specific assays. RESULTS: In glucose-tolerant volunteers, daily administration of L. reuteri SD5865 increased glucose-stimulated GLP-1 and GLP-2 release by 76% (P < 0.01) and 43% (P < 0.01), respectively, compared with placebo, along with 49% higher insulin (P < 0.05) and 55% higher C-peptide secretion (P < 0.05). However, the intervention did not alter peripheral and hepatic insulin sensitivity, body mass, ectopic fat content, or circulating cytokines. CONCLUSIONS: Enrichment of gut microbiota with L. reuteri increases insulin secretion, possibly due to augmented incretin release, but does not directly affect insulin sensitivity or body fat distribution. This suggests that oral ingestion of one specific strain may serve as a novel therapeutic approach to improve glucose-dependent insulin release.
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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 | 0.000 | 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 it