Lower insulin level is associated with sarcopenia in community-dwelling frail and non-frail older adults
Bibliographic record
Abstract
Background Sarcopenia is common among older individuals with and without type 2 diabetes mellitus (T2DM). There are conflicting evidence in support of the role of insulin in the development of age-related and T2DM-related sarcopenia. We investigated the relationships between the levels of fasting insulin and other blood biomarkers related to insulin or lipid metabolism with the presence of sarcopenia in two independent studies. Materials and methods In 246 pre-frail frail older individuals with ( n = 41) and without T2DM ( n = 205) in the Singapore Frailty Interventional Trial, sarcopenia was defined by low appendicular lean mass (ALM) relative to total body mass (skeletal muscle index, SMI = ALM/height 2 ) and low lower limb strength or gait speed according to the Asian Working Group for Sarcopenia (AWGS) criteria released in 2019, and related to levels of fasting insulin and glucose, C-peptide, IGF-1, leptin, and active ghrelin. This investigation was validated in another independent study sample of 189 robust and pre-frail frail elderly in the Singapore Longitudinal Aging Study Wave 2 (SLAS-2). Results Compared to non-sarcopenic individuals, those with sarcopenia and possible sarcopenia showed significantly lower fasting insulin ( p < 0.05) in pre-frail/frail and non-frail older individuals. Consistent trends of relationships were observed for serum levels of C-peptide, IGF-1, leptin, and active ghrelin. In multivariable logistic regression models, sarcopenia was independently associated with low insulin ( p < 0.05). Levels of fasting insulin, C-peptide, and leptin were also significantly associated with BMI, SMI, knee extension strength, gait speed, and physical activity score. Conclusion Dysregulated insulin secretion in diabetic and non-diabetic older individuals may play an important role in age-related and diabetes-related sarcopenia.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".