Role of Peripheral and Central Insulin Resistance in Neuropsychiatric Disorders
Bibliographic record
Abstract
Insulin acts on different organs, including the brain, which helps it regulate energy metabolism. Insulin signaling plays an important role in the function of different cell types. In this review, we have summarized the key roles of insulin and insulin receptors in healthy brains and in different brain disorders. Insulin signaling, as well as insulin resistance (IR), is a major contributor in the regulation of mood, behavior, and cognition. Recent evidence showed that both peripheral and central insulin resistance play a role in the pathophysiology, clinical presentation, and management of neuropsychiatric disorders like Cognitive Impairment/Dementia, Depression, and Schizophrenia. Many human studies point out Insulin Resistance/Metabolic Syndrome can increase the risk of dementia especially Alzheimer's dementia (AD). IR has been shown to play a role in AD development but also in its progression. This review article discusses the pathophysiological pathways and mechanisms of insulin resistance in major neuropsychiatric disorders. The extent of insulin resistance can be quantified using IR biomarkers like insulin levels, HOMA-IR index, and Triglyceride glucose-body mass index (TyG-BMI) levels. IR has been shown to precede neurodegeneration. Human trials showed current treatment with certain antidiabetic drugs, as well as life style management, like weight loss and exercise for IR, have shown promise in the management of cognitive/neuropsychiatric disorders. This may pave the pathway to the development of new therapeutic approaches to these challenging disorders of dementia and psychiatric diseases. Recent clinical trials are showing some encouraging evidence for these pharmacological and nonpharmacological approaches for IR in psychiatric and cognitive disorders, even though more research is needed to apply this evidence into clinical practice. Early identification and management of IR may help as a strategy to potentially alter neuropsychiatric disorders onset as well as its progression.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".