O10.1. CORRELATIONS BETWEEN INSULIN RESISTANCE AND PERIPHERAL IMMUNITY IN FIRST-EPISODE PSYCHOSIS – A GENE EXPRESSION STUDY
Bibliographic record
Abstract
The changes in glucose metabolism and triglycerids are the most consistently reported peripheral findings in the early phase of first-episode psychosis (FEP). Here, we aim at understanding the correlations between insulin resistance and peripheral immunity in FEP. We recruited patients with FEP (n=67) and matched controls (n=38), aged 18–40 years. Fasting peripheral blood samples were collected between 8 and 10 a.m. in 10ml PAXgene tubes. We applied the NanoString nCounter in-solution hybridization technology to determine gene expression levels of 186 candidate genes reflecting activation of the immune system or associated with metabolic changes during anti-psychotics. We present correlations of expression levels with insulin resistance, endogenous insulin secretion (connecting (C)-peptide) and lipids in patients and controls. Initially, the patients as compared to controls had a higher HOMA index (1.7 vs 1.1, p=0.003) and triglycerids (1.11 vs 0.79, p=0.003) and lower levels of HDL (1.36 vs 1.47, p=0.004), reflecting insulin resistance. In patients, markers of the peripheral immune response robustly correlated with higher fasting glucose, HOMA index and triglycerids, and lower HDL. An activation of the peripheral immune system in FEP patients correlated with an abnormal glucose and lipid metabolism. While cardiovascular diseases and cancer are the reasons for excess mortality in psychosis, this novel knowledge could help in targeted add-on treatments to prevent the unfavorable course towards physical complications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".