Determining the Development of Insulin Resistance in Older Adults of the NuAge cohort Using Trajectory Modeling of the Homeostatic Model Assessment of Insulin Resistance Score
Bibliographic record
Abstract
Background: Age-associated body composition changes increase the risk of developing insulin resistance. Identifying these subjects in epidemiological studies is challenging.Objective: Identify insulin-resistant subjects over a 3-year period and determine predictors.Methods: Data on 649 non-diabetic participants of the Quebec Longitudinal Study on Nutrition and Successful Aging (NuAge) Cohort were analyzed. Muscle mass index (kg/height in m²) and %body fat were derived from dual X-ray absorptiometry or bioimpedancemetry. Insulin resistance was based on the Homeostatic Model Assessment of insulin resistance HOMA-IR score. Physical activity was assessed by questionnaire. Protein and fat intake were obtained from three 24-h food recalls. Developmental trajectories over 4 time points were used to determine insulin sensitivity status. Logistic regression analyses serve to determine baseline variables affecting change over time.Results: Seven group-based trajectories were identified from a model with good fit. Curve inspection allowed for the classification of insulin sensitive and resistant subjects. Predictors of insulin resistance were: muscle mass index [OR (95% CI): 1.72 (1.26 - 2.3)]; %body fat [1.18 (1.12 - 1.25)]; male sex [OR for women versus men: 0.145 (0.04 - 0.45)].Conclusion: Greater muscle mass index and % body fat contribute to higher odds of insulin resistance with aging in man whereas being a woman decreases these odds. The relationship between muscle mass and the development of insulin resistance is counterintuitive and requires further exploration since it suggests that maintenance of muscle mass with aging is a contributor. Our probabilistic approach addresses one of the challenges in determining insulin-resistant subjects in epidemiological studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".