Improving risk assessment and familial aggregation of age at onset in schizophrenia using minor physical anomalies and craniofacial measures

Tsai, I-Ning; Lin, Jin-Jia; Lu, Ming-Kun; Tan, Hung-Pin; Jang, Fong-Lin; Gan, Shu‐Ting; Lin, Sheng‐Hsiang

doi:10.1097/md.0000000000004406

Cited by 8 publications

(4 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dysmorphic features, as rated using the Waldrop Physical Anomaly Scale [52,53], are more common in psychosis and bipolar disorder, suggesting similar etiopathogenic origins [31]. Previous literature also indicates a higher prevalence of craniofacial dysmorphology in SZ [30,52,[54][55][56]. Indeed, craniofacial dysmorphology is associated with a two-fold increase in psychosis [30].…”

Section: Discussionmentioning

confidence: 97%

“…Nonetheless, craniofacial dysmorphology in 22q11DS PS + and non-deleted PS likely results from a common developmental mechanism-disrupted migration of neural crest cells [58,59]. Abnormalities in neural crest cells, which are located proximal to the neural tubes, have been implicated in craniofacial dysmorphology [54,56]. Notably, much of the pathology related to congenital physical dysmorphology in 22q11DS can be attributed to complications in morphogenesis and subsequent abnormal function of pharyngeal arch system derivatives, including the craniofacial structures [1].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Computer-vision analysis of craniofacial dysmorphology in 22q11.2 deletion syndrome and psychosis spectrum disorders

Roalf,

McDonald-McGinn,

Jee

et al. 2024

J Neurodevelop Disord

View full text Add to dashboard Cite

Background Minor physical anomalies (MPAs) are congenital morphological abnormalities linked to disruptions of fetal development. MPAs are common in 22q11.2 deletion syndrome (22q11DS) and psychosis spectrum disorders (PS) and likely represent a disruption of early embryologic development that may help identify overlapping mechanisms linked to psychosis in these disorders. Methods Here, 2D digital photographs were collected from 22q11DS (n = 150), PS (n = 55), and typically developing (TD; n = 93) individuals. Photographs were analyzed using two computer-vision techniques: (1) DeepGestalt algorithm (Face2Gene (F2G)) technology to identify the presence of genetically mediated facial disorders, and (2) Emotrics—a semi-automated machine learning technique that localizes and measures facial features. Results F2G reliably identified patients with 22q11DS; faces of PS patients were matched to several genetic conditions including FragileX and 22q11DS. PCA-derived factor loadings of all F2G scores indicated unique and overlapping facial patterns that were related to both 22q11DS and PS. Regional facial measurements of the eyes and nose were smaller in 22q11DS as compared to TD, while PS showed intermediate measurements. Conclusions The extent to which craniofacial dysmorphology 22q11DS and PS overlapping and evident before the impairment or distress of sub-psychotic symptoms may allow us to identify at-risk youths more reliably and at an earlier stage of development.

show abstract

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Computer-vision analysis of craniofacial dysmorphology in 22q11.2 deletion syndrome and psychosis spectrum disorders

Roalf,

McDonald-McGinn,

Jee

et al. 2024

J Neurodevelop Disord

View full text Add to dashboard Cite

show abstract

“…There were many studies that used logistic regression or lasso regression to build nomograms 30 – 32 . Several studies also used logistic regression to determine which variables of MPAs can discriminant schizophrenia patients from healthy controls 10 , 33 , 34 . Lasso regression is a shrinkage and variable selection method for regression models to avoid overfitting the data in the analysis and overestimation of how well the model performs 35 , 36 .…”

Section: Discussionmentioning

confidence: 99%

“…Qualitative minor malformations represent defects of embryogenesis during the process of organogenesis, and the phenogenetic variation of final morphogenesis can be seen through quantitative defects 9 . Most of the current research on MPAs in schizophrenia has been based on qualitative observation, and only a few study groups have conducted quantitative measurements of MPAs 10 . Moreover, although MPAs have been suggested as biomarkers for schizophrenia, studies have rarely performed qualitative and quantitative measurements of MPAs in all six body parts (head, ears, eyes, mouth, hands, and feet) nor have they used a large number of cases for validation.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a web-based prediction tool on minor physical anomalies for schizophrenia

Wang

Lin

et al. 2022

Schizophr

Self Cite

View full text Add to dashboard Cite

In support of the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) have been suggested as biomarkers and potential pathophysiological significance for schizophrenia. However, an integrated, clinically useful tool that used qualitative and quantitative MPAs to visualize and predict schizophrenia risk while characterizing the degree of importance of MPA items was lacking. We recruited a training set and a validation set, including 463 schizophrenia patients and 281 healthy controls to conduct logistic regression and the least absolute shrinkage and selection operator (Lasso) regression to select the best parameters of MPAs and constructed nomograms. Two nomograms were built to show the weights of these predictors. In the logistic regression model, 11 out of a total of 68 parameters were identified as the best MPA items for distinguishing between patients with schizophrenia and controls, including hair whorls, epicanthus, adherent ear lobes, high palate, furrowed tongue, hyperconvex fingernails, a large gap between first and second toes, skull height, nasal width, mouth width, and palate width. The Lasso regression model included the same variables of the logistic regression model, except for nasal width, and further included two items (interpupillary distance and soft ears) to assess the risk of schizophrenia. The results of the validation dataset verified the efficacy of the nomograms with the area under the curve 0.84 and 0.85 in the logistic regression model and lasso regression model, respectively. This study provides an easy-to-use tool based on validated risk models of schizophrenia and reflects a divergence in development between schizophrenia patients and healthy controls (https://www.szprediction.net/).

show abstract

Minor physical anomalies may be related to treatment resistance in patients with schizophrenia

Uğurpala,

Berberoğlu,

Özkan

et al. 2023

Asian Journal of Psychiatry

View full text Add to dashboard Cite

Improving risk assessment and familial aggregation of age at onset in schizophrenia using minor physical anomalies and craniofacial measures

Abstract: Supplemental Digital Content is available in the text

Cited by 8 publications

References 57 publications

Computer-vision analysis of craniofacial dysmorphology in 22q11.2 deletion syndrome and psychosis spectrum disorders

Computer-vision analysis of craniofacial dysmorphology in 22q11.2 deletion syndrome and psychosis spectrum disorders

Development and validation of a web-based prediction tool on minor physical anomalies for schizophrenia

Minor physical anomalies may be related to treatment resistance in patients with schizophrenia

Contact Info

Product

Resources

About