Insulin Prevents Fat Loss and Promotes Muscle Loss During Intermittent Fasting in Obesity
Bibliographic record
Abstract
Abstract Elevated blood glucose, insulin, and insulin resistance are associated with obesity and type 2 diabetes (T2D). High blood insulin levels blunt lipolysis and promote lipogenesis, and thus weight gain. Intermittent fasting (IF) has emerged as a weight loss strategy that also lowers blood glucose and improves insulin resistance. However, some people with obesity, or T2D have less fat loss and more lean mass loss after IF. It is not known why some people lose more fat or muscle during IF. We hypothesized that features of obesity, such as high insulin and insulin action in adipocytes dictate less adipose loss and more muscle loss during IF. In humans, we found that people living with obesity and higher blood insulin lost more lean mass after a 48-hour fast. Chronic elevation of insulin in obese mice lowered adipose loss and promoted muscle loss after 10 weeks of 5:2 IF in obese mice, while concurrently lowering adipose tissue interferon regulatory factor 4 (IRF4) expression. Whole-body and adipocyte-specific deletion of Irf4 in mice phenocopied chronic hyperinsulinemia, resulting in less fat loss and greater muscle loss after 10 weeks of 5:2 IF, which occurred in mouse models of equal and reduced caloric intake during IF. Therefore, hyperinsulinemia and suppression of adipocyte IRF4 promote muscle loss over fat loss during IF. Significance Statement It is not known why some people lose muscle during IF. In humans, we found that high blood insulin during obesity correlated with increased lean mass loss after one 48-hour fast. Mechanistically, we found that hyperinsulinemia and the insulin responsive factor IRF4 within adipocytes as regulators of adipose and muscle loss during chronic IF obese mice. Therefore, insulin status and regulation of adipocyte IRF4 may be important factors to consider before prescribing IF for weight loss as lower muscle mass is known to be detrimental to metabolic and overall health status.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".