A pathway phenotype linking metabolic, immune, oxidative, and opioid pathways with comorbid depression, atherosclerosis, and unstable angina.

BACKGROUND: There is strong comorbidity between atherosclerosis (ATS) and depression which is attributed to increased atherogenicity, insulin resistance (IR), and immune and oxidative stress. AIM OF THE STUDY: To examine the role of the above pathways and mu-opioid receptor (MOR), ß-endorphin levels, zinc, copper, vitamin D3, calcium, and magnesium in depression due to ATS/unstable angina (UA). METHODS: Biomarkers were assayed in 58 controls and 120 ATS patients divided into those with moderate and severe depression according to the Beck Depression Inventory-II (BDI-II) scores >19 and >29, respectively. RESULTS: Neural network and logistic regression models showed that severe depression due to ATS/UA was best predicted by interleukin-6 (IL-6), UA, MOR, zinc, ß-endorphin, calcium and magnesium, and that moderate depression was associated with IL-6, zinc, MOR, ß-endorphin, UA, atherogenicity, IR, and calcium. Neural networks yielded a significant discrimination of severe and moderate depression with an area under the receiver operating curves of 0.831 and 0.931, respectively. Using Partial Least Squares path analysis, we found that 66.2% of the variance in a latent vector extracted from ATS/UA clinical features, and the BDI-II scores, atherogenicity, and IR could be explained by the regression on IL-6, IL-10, zinc, copper, calcium, MOR, and age. The BDI-II scores increased from controls to ATS to UA class III to UA class IV. CONCLUSIONS: Immune activation, the endogenous opioid system, antioxidants, trace elements, and macrominerals modulate a common core shared by increased depressive symptoms, ATS, UA, atherogenicity, and IR.

In schizophrenia, non-remitters and partial remitters to treatment with antipsychotics are qualitatively distinct classes with respect to neurocognitive deficits and neuro-immune biomarkers: results of soft independent modeling of class analogy.

Around one third of schizophrenia patients are non-responders to antipsychotic therapy. The present study aimed to delineate the pathway-phenotypes of non-remitters (NRTT) and partial remitters (PRTT) to treatment with antipsychotics as defined using the Global Clinical Impression scales. We recruited 60 NRTT, 50 PRTT and 43 healthy controls and measured schizophrenia symptoms, neurocognitive tests, plasma CCL11, interleukin-(IL)-6, IL-10, Dickkopf protein 1 (DKK1), high mobility group box-1 protein (HMGB1), κ- and µ-opioid receptors (KOR and MOR, respectively), endomorphin-2 (EM-2), and ß-endorphin. Soft independent modeling of class analogy (SIMCA) showed that NRTT and PRTT are significantly discriminated with a cross-validated accuracy of 94.7% and are qualitatively distinct classes using symptomatome, and neuro-immune-opioid-cognitome (NIOC) features as modeling variables. Moreover, a NIOC pathway phenotype discriminated PRTT from healthy controls with an accuracy of 100% indicating that PRTT and controls are two qualitative distinct classes. Using NIOC features as discriminatory variables in SIMCA showed that all PRTT were rejected as belonging to the normal control class and authenticated as belonging to their target class. In conclusion, a non-response to treatment can best be profiled using a SIMCA model constructed using symptomatome and NIOC features. A partial response should be delineated using SIMCA by authenticating patients as controls or PRTT instead of using scale-derived cut-off values or a number of scale items being rated mild or better. The results show that PRTT is characterized by an active NIOC pathway phenotype and that both NRTT and PRTT should be treated by targeting neuro-immune and opioid pathways.

Trace element, immune and opioid biomarkers of unstable angina, increased atherogenicity and insulin resistance: Results of machine learning.

BACKGROUND: Aberrations in endothelial cells, immune and oxidative pathways are associated with atherosclerosis (ATS) and unstable angina (UA). The role of trace elements, minerals, and the endogenous opioid system (EOS) in UA are less well established. METHODS: We measured lipid, insulin resistance (IR), and immune, trace element (copper and zinc), mineral (magnesium, calcium), EOS (ß-endorphin and mu-opioid receptor (MOR)) and antioxidant (vitamin D3) biomarkers in patients with ATS (n = 60) and UA (n = 60) and healthy controls (n = 58). RESULTS: ATS patients showed increased atherogenic and IR indices, IL-6, IL-10, ß-endorphin, copper and magnesium, and lower zinc than healthy controls. Logistic regression showed that UA was significantly discriminated from ATS without UA with an accuracy of 85.5 % using calcium, IL-10, ß-endorphin, MOR, triglycerides, IR (all positively), and copper and vitamin D3 (inversely). Neural networks showed that UA was discriminated from ATS without UA with an area under the ROC curve of 0.942 using MOR, ß-endorphin, calcium, insulin resistance, vitamin D3 and copper as input variables. We found that 50.0 % of the variance in IR was explained by the regression on copper, IL-10, IL-6 (all positively), and zinc (inversely), while 32.9 % of the variance in the atherogenic index of plasma was explained by copper, IL-10 (both positively), and magnesium (inversely). CONCLUSION: UA is not only mediated by insulin resistance, atherogenicity, and immune disorders, but also by aberrations in the endogenous opioid system and trace elements as well as lowered antioxidant levels. Copper appears to play a key role in IR and atherogenicity.

Chronic fatigue syndrome and fibromyalgia-like symptoms are an integral component of the phenome of schizophrenia: neuro-immune and opioid system correlates.

Physiosomatic symptoms are an important part of schizophrenia phenomenology. The aim of this study is to examine the biomarker, neurocognitive and symptomatic correlates of physiosomatic symptoms in schizophrenia. We recruited 115 schizophrenia patients and 43 healthy controls and measured the Fibromyalgia and Chronic Fatigue Syndrome Rating (FF) scale, schizophrenia symptom dimensions, and the Brief Assessment of Cognition in Schizophrenia. We measured neuro-immune markers including plasma CCL11 (eotaxin), interleukin-(IL)-6, IL-10, Dickkopf protein 1 (DKK1), high mobility group box 1 protein (HMGB1) and endogenous opioid system (EOS) markers including κ-opioid receptor (KOR), µ-opioid receptor (MOR), endomorphin-2 (EM2) and ß-endorphin. Patients with an increased FF score display increased ratings of psychosis, hostility, excitement, formal though disorders, psycho-motor retardation and negative symptoms as compared with patients with lower FF scores. A large part of the variance in the FF score (55.1%) is explained by the regression on digit sequencing task, token motor task, list learning, IL-10, age (all inversely) and IL-6 (positively). Neural network analysis shows that the top-6 predictors of the FF score are (in descending order): IL-6, HMGB1, education, MOR, KOR and IL-10. We found that 45.1% of the variance in a latent vector extracted from cognitive test scores, schizophrenia symptoms and the FF score was explained by HMGB1, MOR, EM2, DKK1, and CCL11. Physiosomatic symptoms are an integral part of the phenome of schizophrenia. Neurotoxic immune pathways and lowered immune regulation coupled with alterations in the EOS appear to drive the physiosomatic symptoms of schizophrenia.

High Mobility Group Protein 1 and Dickkopf-Related Protein 1 in Schizophrenia and Treatment-Resistant Schizophrenia: Associations With Interleukin-6, Symptom Domains, and Neurocognitive Impairments.

BACKGROUND: Schizophrenia (SCZ) and treatment-resistant schizophrenia (TRS) are associated with aberrations in immune-inflammatory pathways. Increased high mobility group protein 1 (HMGB1), an inflammatory mediator, and Dickkopf-related protein (DKK1), a Wnt/ß-catenin signaling antagonist, affect the blood-brain barrier and induce neurotoxic effects and neurocognitive deficits. AIM: The present study aims to examine HMGB1 and DDK1 in nonresponders to treatments (NRTT) with antipsychotics (n = 60), partial RTT (PRTT, n = 55), and healthy controls (n = 43) in relation to established markers of SCZ, including interleukin (IL)-6, IL-10, and CCL11 (eotaxin), and to delineate whether these proteins are associated with the SCZ symptom subdomains and neurocognitive impairments. RESULTS: HMGB1, DKK1, IL-6, and CCL11 were significantly higher in SCZ patients than in controls. DKK1 and IL-6 were significantly higher in NRTT than in PRTT and controls, while IL-10 was higher in NRTT than in controls. Binary logistic regression analysis showed that SCZ was best predicted by increased DDK1 and HMGB1, while NRTT (vs PRTT) was best predicted by increased IL-6 and CCL11 levels. A large part of the variance in psychosis, hostility, excitation, mannerism, and negative (PHEMN) symptoms and formal thought disorders was explained by HMGB1, IL-6, and CCL11, while most neurocognitive functions were predicted by HMGB1, DDK1, and CCL11. CONCLUSIONS: The neurotoxic effects of HMGB1, DKK1, IL-6, and CCL11 including the effects on the blood-brain barrier and the Wnt/ß-catenin signaling pathway may cause impairments in executive functions and working, episodic, and semantic memory and explain, in part, PHEMN symptoms and a nonresponse to treatment with antipsychotic drugs.