F22. DYSLIPIDEMIA AND INFLAMMATORY MARKERS IN RELATION TO CLINICAL SYMPTOMATOLOGY IN PSYCHOTIC DISORDERS
Bibliographic record
Abstract
Although the role of lipid disturbances and inflammation in psychotic disorders have been demonstrated in a substantial body of research, the association between clinical symptomatology and these two important pathways has not been studied in detail. The main aim of this study was to investigate the associations between serum lipid levels [total cholesterol (TC), low density lipoprotein (LDL), triglyceride (TG)]; general or specific inflammatory markers [C-reactive protein (CRP), soluble tumor necrosis factor receptor 1(sTNF-R1), osteoprotegerin (OPG), interleukin 1 receptor antagonist (IL-1Ra)]; and clinical symptoms (positive, negative and depressive) in patients with psychotic disorders. The sample is consisted of 652 participants divided in two groups: Schizophrenia, schizophreniform and schizoaffective, (schizophrenia group, N = 344); psychosis NOS, psychotic bipolar I, II and NOS, (non-schizophrenia group, N = 308) recruited consecutively between 2003 and 2015 from five major hospitals in Oslo, Norway, as part of Thematically Organized Psychosis (TOP) Study. The Regional Committee for Medical Research Ethics approved the study. Demographic, clinical and medications data were obtained by clinical interviews and from medical records. SCID-I was used for diagnosis in addition to Positive and Negative Syndrome Scale (PANSS) and Calgary Depression Scale for Schizophrenia (CDSS) to assess symptoms severity. Bivariate and multivariate analyses were performed to evaluate associations between symptom profiles, lipid levels and inflammatory markers. Schizophrenia group showed higher levels of TC and LDL compared to non-schizophrenia group after adjusting for age, gender, BMI, smoking, and medications. TC and LDL were positively correlated with depression, whereas TG and LDL were positively correlated with negative symptoms. CRP and OPG were significantly associated with higher levels of TC and LDL. While, sTNF-R1 showed significant positive correlation only with TG. In multivariate regression, higher LDL was significantly associated with higher age, BMI, depressive severity in addition to two inflammatory markers CRP and OPG. The findings of this study highlight the importance of understanding the interaction between inflammatory markers and lipid, and their relation to clinical profile especially depression in psychotic disorders
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.007 | 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".