Multiple Lifestyle Interventions: Impact On Metabolic Health Of Early Insulin-Resistant Patients
Bibliographic record
Abstract
Early insulin resistance (IR) is a condition characterized by the detection of hyperinsulinemia without dysglycemia, which can be measured by the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). However, it frequently goes under the radar until further advanced stages develop. Early detection of IR and implementation of preventive strategies at this early stage could prevent numerous cardiometabolic conditions, including progression to type 2 diabetes. PURPOSE: To evaluate the effects of multiple lifestyle interventions in a clinical setting on the metabolic health of early insulin-resistant patients. METHODS: Between November 2021 and August 2022, 71 individuals with basal HbA1c ≤ 5.7 % and basal HOMA-IR values ≥3 accepted to participate in the diabetes prevention clinic at Montreal Heart Institute. The intervention consists of educational and nutritional counseling, promoting a Mediterranean diet and an 8:16 time-restricted eating, along with personalized physical activity prescription, consisting of sessions of aerobic and resistance training. Anthropometric measurements are taken by bioimpedance and blood samples are drawn at 0, 3, 6, and 12 months. Differences in means across variables with repeated observations between baseline and 3 months of intervention were assessed with ANOVA. RESULTS: 45 participants with full data were included in this analysis (mean HbA1c of 5.3% ± 0.2 and mean HOMA-IR of 6.2 ± 3.1 at baseline). Statistically significant changes (p < 0.05) are reported in Table 1. There was a statistically significant strong correlation between both fat mass loss and percentage of fat mass loss and the improvement of the HOMA-IR index between baseline and the follow-up at 3 months (r = 0.6, p < 0.05). CONCLUSION: The results of this study suggest that prioritizing lifestyle changes in these participants with IR improves anthropometric and insulin resistance measures that could lead to preventing the development of type 2 diabetes.
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How this classification was reachedexpand
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".