Association between serum lipid levels, osteoprotegerin and depressive symptomatology in psychotic disorders
Bibliographic record
Abstract
Although the relationship between positive and negative symptoms of psychosis and dyslipidemia has been thoroughly investigated in recent studies, the potential link between depression and lipid status is still under-investigated. We here examined the association between lipid levels and depressive symptomatology in patients with psychotic disorders, in addition to their possible inflammatory associations. Participants (n = 652) with the following distribution: schizophrenia, schizophreniform and schizoaffective disorder (schizophrenia group, n = 344); bipolar I, II, NOS, and psychosis NOS (non-schizophrenia group, n = 308) were recruited consecutively from the Norwegian Thematically Organized Psychosis (TOP) Study. Clinical data were obtained by Positive and Negative Syndrome Scale (PANSS), and Calgary Depression Scale for Schizophrenia (CDSS). Blood samples were analyzed for total cholesterol (TC), low-density lipoprotein (LDL), triglyceride (TG), C-reactive protein (CRP), soluble tumor necrosis factor receptor 1(sTNF-R1), osteoprotegerin (OPG), and interleukin 1 receptor antagonist (IL-1Ra). After adjusting for age, gender, BMI, smoking, and dyslipidemia-inducing antipsychotics, TC and LDL scores showed significant associations with depression [β = 0.13, p = 0.007; β = 0.14, p = 0.007], and with two inflammatory markers: CRP [β = 0.14, p = 0.007; β = 0.16, p = 0.007] and OPG [β = 0.14, p = 0.007; β = 0.11, p = 0.007]. Total model variance was 17% for both analyses [F(12, 433) = 8.42, p < 0.001; F(12, 433) = 8.64, p < 0.001]. Current findings highlight a potential independent role of depression and inflammatory markers, CRP and OPG in specific, in the pathophysiology of dyslipidemia 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".