A recent study published in Psychiatry Research has spotlighted minute differences in facial shapes that could potentially be tied to the diagnosis of schizophrenia and bipolar disorder. The research indicates that these facial patterns also correlate with certain aspects of cerebral cortex measurements. By employing advanced geometric morphometric and neuroimaging techniques, the study not only furthers our understanding of the origins of these psychiatric conditions but also paves the way for advancements in personalized medicine.
The research is spearheaded by teams from the Faculty of Biology at the University of Barcelona, the FIDMAG Hermanas Hospitalarias Research Foundation, the Networking Biomedical Research Centre in Mental Health (CIBERSAM), and La Salle Ramon Llull University. Additionally, the Benito Menni CASM and Mare de Déu de la Mercè (Hermanas Hospitalarias) hospitals have lent their support.
Schizophrenia and bipolar disorder are severe psychiatric disorders afflicting approximately 65 million people globally. The presence of a complex mix of symptoms, along with great variability in treatment courses and responses, makes deciphering their causes and achieving accurate diagnoses a daunting task.
Both environmental and genetic factors shape the development and maturation of the brain. In some instances, slight alterations during this process may heighten the risk of psychiatric disorders. In light of this, the study capitalizes on the shared embryonic development of the face and brain, identifying facial shape as an indirect indicator of brain changes related to these diagnoses, while also suggesting the existence of sex-specific patterns.
In the case of schizophrenia, the study detected significant facial distinctions between control subjects and patients, as well as gender-based differences. Notably, for women, schizophrenia diagnosis accounted for a higher proportion of facial shape variation (5.9%) compared to men (4.2%). Conversely, in bipolar disorder, significant facial differences were only observed in male patients.
The study’s first author, Noemí Hostalet (UB, FIDMAG, CIBERSAM), elaborates, “The facial features associated with schizophrenia and bipolar disorder are extremely subtle and imperceptible to the naked eye.” Researchers Neus Martínez-Abadías (UB) and Mar Fatjó-Vilas (UB, FIDMAG, CIBERSAM) further state, “Facial features alone would not suffice for diagnosing these disorders. The same holds true for other brain morphological features and genetic traits. No single biomarker possesses adequate diagnostic potency.”
The research team posits, “The presumption is that by amalgamating the potential of facial, brain, and genetic biomarkers, we could devise a supplementary tool to the clinical interview, enabling clinicians to make diagnoses more swiftly and accurately. Hence, this study could offer a prospective complementary means to current methods for earlier and more accurate diagnoses, provided that stringent ethical and privacy safeguards are in place.”
To implement this biomedical tool effectively, further research and the establishment of highly secure data protection protocols are imperative to confine its use to the medical realm, ensuring compliance with all ethical and privacy norms. In this line of research, preventing the misuse of highly sensitive data, which could lead to discrimination against those with mental disorder diagnoses, is of paramount importance.
Presently, the team is endeavoring to broaden and replicate the analyses using a larger sample population. Concurrently, novel strategies to integrate diverse facial and brain markers with genomic data are also under exploration. By fusing biological, biometric, and clinical markers, the team aspires to deepen our comprehension of the origins of mental disorders and develop tools to assist in their diagnosis.
For those interested, a downloadable PDF copy of the study is available, and an eBook titled “Using iPSCs models for human disease research and drug discovery” can be accessed, which explores how human iPSCs models can enhance drug discovery by providing more accurate, scalable, and demographically representative models, improving patient stratification and reducing late-stage drug failures.
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