Elevated Gluconeogenesis in Aging and Lung Cancer is Related to Inflammation and Blunted Insulin‐Induced Protein Anabolism
Bibliographic record
Abstract
Cancer and aging are both accompanied by metabolic alterations leading to muscle loss that may result from an increased use of amino acids for gluconeogenesis (GNG). Methods we measured the fractional contributions (%) of glycogen, glycerol and GNG from phosphoenolpyruvate (PEP) to 17h‐fasting endogenous glucose production (EGP), using oral 2 H 2 0 and deuterium enrichment of plasma glucose, by NMR spectroscopy. Whole‐body protein and glucose kinetics (1‐ 13 C‐leucine and 3 H 3 ‐glucose) were measured fasting and during a subsequent hyperinsulinemic, euglycemic, isoaminoacidemic clamp in: young (Y, n=11; 27±3 y), older (O, n=13; 65±2 y) and older men with lung cancer (CA, n=9; 66±2 y). Results Men with CA had greater serum CRP levels and resting energy expenditure (REE) than Y and O, and a lesser clamp glucose disposal, indicating insulin resistance. EGP was elevated with aging and cancer with greater contribution from GNG: Y:37±2, O:43±2 and CA:48±4% (ANOVA p<0.05); % contribution from glycogen and glycerol did not differ. The resulting GNG flux (EGP × %GNG) correlated positively with CRP (r=0.69) and REE (r=0.61), and negatively with fat‐free mass index (r=−0.41), all p<0.05. GNG flux was related to lesser glucose uptake and protein anabolic response (r= −0.54, p=0.001) in response to insulin. Conclusions Inflammation may underlie the greater production of glucose from GNG and blunted protein anabolism. (CIHR)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".