Multiple Lifestyle Interventions: Impact On Metabolic Health In Patients With Prediabetes And T2D
Bibliographic record
Abstract
BACKGROUND: In November 2021 the Montreal heart institute’s ÉPIC Center launched a diabetes prevention clinic that suggest a 12-months non-pharmacological intervention approach for people with PRED and T2D. We present a preliminary analysis of the evolution of metabolic health and glycemic control at 3 months. METHODS: The intervention consisted of educational and nutritional counseling, promoting a moderate carbohydrate Mediterranean diet and an 8:16 time-restricted eating, in combination with personalized exercise training prescription. Body composition measurements were taken by bioimpedance, and blood samples were drawn at 0 and 3 months. Data were compared pre and post program using an ANOVA with repeated measures at 3 months. RESULTS: 91 participants have been included in this first exploratory analysis. We observed a change in body mass loss (-2.4 kg; p < 0.01), waist circumference (-3.0 cm; p < 0.01), fat mass loss (-1.7 kg; p < 0.01), visceral fat volume (-0.6 L; p < 0.01), fasting insulin (-17.2 μmol/L; p = 0.01), HOMA-IR (-0.9; p = 0.03), and low-density lipoproteins (-0.2 mmol/L; p < 0.01) (Table 1). At 3 months 43.4% participants with PRED presented with normal glucose concentrations HbA1c < 5.7%. At 3 months, 71.1% participants with T2D have a glycemic control <6.5% (Table 2). CONCLUSIONS: Prioritizing combined lifestyle changes improves metabolic health, even to the point of achieving normal glucose concentrations measured by HbA1c after 3 months. A longer-term analysis will be needed to reach the criteria of remission, as changes in HbA1c should persist beyond 3 months.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".