Glycated hemoglobin, type 2 diabetes, and poor diabetes control are positively associated with impulsivity changes in aged individuals with overweight or obesity and metabolic syndrome
Bibliographic record
Abstract
Impulsivity has been proposed to have an impact on glycemic dysregulation. However, it remains uncertain whether an unfavorable glycemic status could also contribute to an increase in impulsivity levels. This study aims to analyze associations of baseline and time-varying glycemic status with 3-year time-varying impulsivity in older adults at high risk of cardiovascular disease. A 3-year prospective cohort design was conducted within the PREDIMED-Plus-Cognition substudy. The total population includes 487 participants (mean age = 65.2 years; female = 50.5%) with overweight or obesity and metabolic syndrome. Insulin resistance (HOMA-IR), glycated hemoglobin (HbA1c), presence of type 2 diabetes mellitus, and type 2 diabetes control were evaluated. Impulsivity was measured using the Impulsive Behavior Scale questionnaire and various cognitive measurements. Impulsivity z-scores were generated to obtain Global, Trait, and Behavioral Impulsivity domains. Linear mixed models were used to study the longitudinal associations across baseline, 1-year, and 3-year follow-up visits. HOMA-IR was not significantly related to impulsivity. Participants with higher HbA1c levels, type 2 diabetes, and poor control of diabetes showed positive associations with the Global Impulsivity domain over time, and those with higher HbA1c levels were further related to increases in the Trait and Behavioral Impulsivity domains over the follow-up visits. These results suggest a potential positive feedback loop between impulsivity and glycemic-related dysregulation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".