Minor Physical Anomalies Among Schizophrenic Patients as a Biomarker of Its Developmental Origin in Northwest Ethiopia

Minor physical anomalies in schizophrenia and first-degree relatives in comparison to healthy controls: A systematic review and meta-analysis

Minor physical anomalies (MPAs) are anatomical variations that are markers of aberrant early neurodevelopment. Schizophrenia is associated with increased MPA frequency, however, the frequency and distribution of MPAs exhibit substantial heterogeneity in schizophrenia and are not exclusive to this disorder. MPAs at different localizations might represent different developmental origins and might be related to latent genetic predisposition or vulnerability to develop full-blown psychosis. Therefore, we conducted a thorough review of minor physical anomalies (MPAs) in schizophrenia (Sch) and first-degree relatives (SchRel). Analyzing 52 studies published from January 1980 to October 2023, the meta-analysis compared MPA scores between 3780 schizophrenia patients and 3871 controls, as well as 1415 SchRel and 1569 controls. The total MPA score was significantly increased in schizophrenia compared to controls (g = 0.78 [0.63-0.93], p<0.001). In regional MPA meta-analyses, effect sizes ranged from 0.56 to 0.78. The difference between SchRel and controls was moderate (g = 0.44 [0.28-0.61], p<0.001). When individual MPA items were analyzed separately, fine electric hair, malformed ear, asymmetrical ear, curved 5th finger were anomalies that were shared between both schizophrenia and SchRel. Also, direct comparisons of the frequency of MPAs in schizophrenia and their relatives were conducted. Additionally, the early age of onset of schizophrenia was associated with mouth anomalies (Z=-2.13, p = 0.03), and ear anomalies were associated with a higher percentage of males in the schizophrenia group (Z = 2.64, p = 0.008). These findings support the notion that different MPAs might be associated with genetic susceptibility as well as vulnerability to developing full-blown psychosis. Studies investigating clinical and neurobiological correlates of MPAs in schizophrenia might be helpful in characterizing subtypes of psychoses that are associated with different developmental processes.

Machine learning for prediction of schizophrenia based on identifying the primary and interaction effects of minor physical anomalies

In the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) are considered neurodevelopmental markers of schizophrenia. To date, there has been no research to evaluate the interaction between MPAs. Our study built and used a machine learning model to predict the risk of schizophrenia based on measurements of MPA items and to investigate the potential primary and interaction effects of MPAs. The study included 470 patients with schizophrenia and 354 healthy controls. The models used are classical statistical model, Logistic Regression (LR), and machine leaning models, Decision Tree (DT) and Random Forest (RF). We also plotted two-dimensional scatter diagrams and three-dimensional linear/quadratic discriminant analysis (LDA/QDA) graphs for comparison with the DT dendritic structure. We found that RF had the highest predictive power for schizophrenia (Full-training AUC = 0.97 and 5-fold cross-validation AUC = 0.75). We identified several primary MPAs, such as the mouth region, high palate, furrowed tongue, skull height and mouth width. Quantitative MPA analysis indicated that the higher skull height and the narrower mouth width, the higher the risk of schizophrenia. In the interaction, we further identified that skull height and mouth width, furrowed tongue and skull height, high palate and skull height, and high palate and furrowed tongue, showed significant two-item interactions with schizophrenia. A weak three-item interaction was found between high palate, skull height, and mouth width. In conclusion, we found that the two machine learning methods showed good predictive ability in assessing the risk of schizophrenia using the primary and interaction effects of MPAs.