Relation of Insulin Resistance to Brain Glucose Metabolism in Fasting and Hyperinsulinemic States: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
CONTEXT: Abnormal brain glucose metabolism may cause cognitive disease in type 2 diabetes, yet the relation between insulin resistance and brain glucose metabolism has not been systematically described. OBJECTIVE: We evaluated the impact of metabolic condition (fasting vs insulin stimulation, eg, from hyperinsulinemic clamp) on the association between insulin resistance of different etiologies and brain glucose metabolism. DATA SOURCES: PubMed, Embase, Cochrane Library, and Web of Science were systematically searched from inception until February 2022. STUDY SELECTION: Of 656 unique records, we deemed 31 eligible. Criteria were studies assessing brain glucose metabolism (uptake or metabolic rate) by 18F-2-fluoro-2-deoxy-D-glucose-positron emission tomography in individuals characterized by measures of or clinical proxies for insulin resistance (eg, type 2 diabetes and obesity). DATA EXTRACTION: Two independent investigators extracted data and assessed study quality. DATA SYNTHESIS: We applied random-effects models to pool Hedge's g standardized mean differences. Insulin resistance was associated with decreased brain glucose metabolism during fasting [-0.47 SD, 95% confidence interval (CI): -0.73 to -0.22, P < .001, I2 = 71%] and increased metabolism during insulin stimulation (1.44 SD, 95% CI 0.79 to 2.09, P = .002, I2 = 43%). Contrary to type 2 diabetes and other insulin resistance-related conditions, obesity was not associated with brain hypometabolism in fasting states (0.29 SD, 95% CI -.81 to 1.39). CONCLUSION: Metabolic conditions modify associations between insulin resistance and brain glucose metabolism; ie, most individuals with insulin resistance display hypometabolism during fasting and hypermetabolism during insulin stimulation.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".