9179 Type 2 Diabetes Alters Metabolic Fuel Selection During Intermittent Fasting
Bibliographic record
Abstract
Abstract Disclosure: M.O. Conn: None. D.M. Marko: None. J.D. Schertzer: None. Type 2 Diabetes Alters Metabolic Fuel Selection During Intermittent Fasting Diabetes is characterized by elevated blood glucose resulting from defective insulin secretion and/or insulin resistance. Intermittent fasting, a popular weight loss strategy involving periodic reduction of caloric intake, can improve glycemia. Fasting shifts metabolism from carbohydrates to free fatty acids and ketones. However, high blood insulin can inhibit lipolysis and alter metabolic substrate selection in prediabetes, resulting in muscle catabolism to maintain energy homeostasis. Clinical studies have observed a significant decrease in muscle mass in subjects with obesity and diabetes. Our research aims to characterize lean mass versus fat loss after intermittent fasting in a mouse model of obesity and type 2 diabetes (T2D) and investigate the role of muscle alanine catabolism. We hypothesized that intermittent fasting would promote less lipolysis and more muscle alanine catabolism, causing more muscle loss in diabetic mice than controls. We compared male db/db mice manifesting obesity, hyperglycemia, and hyperinsulinemia to age-matched db/+ (control) mice. Our lab established a 5:2 repeated fasting protocol of ad-libitum access to food for 5 days a week and 2 days of fasting on non-consecutive days. Blood glucose was monitored weekly in fed and fasted states to assess glycemic control. While intermittent fasting lowered fasted blood glucose in both groups, no significant body weight or metabolic tissue mass changes were observed. Metabolic cage data demonstrated increased reliance on fat-based substrates for energy in control mice that underwent intermittent fasting during feeding and fasting periods. Diabetic mice did not show significant changes in the fasted state, but intermittent fasting led to increased reliance on carbohydrate oxidation during re-feeding periods, indicating impaired metabolic flexibility. After 10 weeks, the mice were sacrificed, and metabolic tissue histology (adipose, liver, skeletal muscle) will be performed. We will also analyze serum markers (alanine, fatty acids, glycerol, glucose, insulin) and the activity of the metabolic enzyme alanine transaminase (ALT) in the muscle and liver. This experiment will improve our understanding of how lipolysis and protein breakdown in muscle regulate lean mass versus fat mass loss in obesity and T2D, leading to more effective tailoring of fasting to specific populations. Presentation: 6/3/2024
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".