A Novel Pathway Phenotype of Temporal Lobe Epilepsy and Comorbid Psychiatric Disorders: Results of Precision Nomothetic Medicine

Paraoxonase 1 status is a major Janus-faced component of mild and moderate acute ischemic stroke and consequent disabilities

AIMS

This study aims to examine the associations between paraoxonase 1 (PON)1 status and acute ischemic stroke (AIS) and consequent disabilities.

METHODS

This study recruited 122 patients with AIS and 40 healthy controls and assessed the Q192R gene variants, arylesterase (AREase) and chloromethyl phenylacetate (CMPAase) activities, and high-density lipoprotein cholesterol (HDLc) in baseline conditions. AREase and CMPAase were measured 3 months later. The National Institutes of Health Stroke Scale (NIHSS) and the modified Rankin score (mRS) were assessed at baseline and 3 and 6 months later.

RESULTS

Reduced CMPAase and increased AREase activities are significantly associated with AIS and mRS and NIHSS scores (baseline and 3 and 6 months later). The best predictor of AIS/disabilities was a decrease in the z-unit-based composite zCMPAase-zAREase score. Serum high density lipoprotein cholsterol (HDLc) was significantly correlated with CMPAase, but not AREase, activity and a lowered zCMPAase + zHDLc score was the second best predictor of AIS/disabilities. Regression analysis showed that 34.7% of the variance in baseline NIHSS was explained by zCMPAase-zAREase and zCMPAase + zHDLc composites, HDLc, and hypertension. Neural network analysis showed that stroke was differentiated from controls with an area under the ROC curve of 0.975 using both new composite scores, PON1 status, hypertension, dyslipidemia, previous stroke as body mass index. The PON1 Q192R genotype has many significant direct and mediated effects on AIS/disabilities, however, its overall effect was not significant.

DISCUSSION

PON1 status and the CMPAase-HDLc complex play key roles in AIS and its disabilities at baseline and 3 and 6 months later.

Research and Diagnostic Algorithmic Rules (RADAR) for mood disorders, recurrence of illness, suicidal behaviours, and the patient's lifetime trajectory

The top-down Diagnostic and Statistical Manual/International Statistical Classification of Diseases categories of mood disorders are inaccurate, and their dogmatic nature precludes both deductive (as indisputable) and inductive (as top-down) remodelling of case definitions. In trials, psychiatric rating scale scores employed as outcome variables are invalid and rely on folk psychology-like narratives. Using machine learning techniques, we developed a new precision nomothetic model of mood disorders with a recurrence of illness (ROI) index, a new endophenotype class, namely Major Dysmood Disorder (MDMD), characterised by increased ROI, a more severe phenome, and more disabilities. Nonetheless, our previous studies did not compute Research and Diagnostic Algorithmic Rules (RADAR) to diagnose MDMD and score ROI, lifetime (LT), and current suicidal behaviours, as well as the phenome of mood disorders. Here, we provide rules to compute bottom-up RADAR scores for MDMD, ROI, LT and current suicidal ideation and attempts, the phenome of mood disorders, and the lifetime trajectory of mood disorder patients from a family history of mood disorders and substance abuse to adverse childhood experiences, ROI, and the phenome. We also demonstrate how to plot the 12 major scores in a single RADAR graph, which displays all features in a two-dimensional plot. These graphs allow the characteristics of a patient to be displayed as an idiomatic fingerprint, allowing one to estimate the key traits and severity of the illness at a glance. Consequently, biomarker research into mood disorders should use our RADAR scores to examine pan-omics data, which should be used to enlarge our precision models and RADAR graph.