The Ulnar Left Second Digital Ridge Count Poorly Predicts the Risk for Schizophrenia

Abstract Background Anthropometric measurements, reflecting the interplay of nutritional, environmental, and genetic factors, are commonly used to study human physical traits. Despite previous research suggesting their potential as indicators of neurodevelopmental processes and genetic influences, their role in predicting schizophrenia risk remains uncertain. This study aims to address this gap by investigating the predictive value of the ulnar left second digital ridge count in assessing the risk for schizophrenia, contributing to our understanding of the association between anthropometric variables and schizophrenia risk. Method Digit lengths were measured from the basal crease of the digit to the fingertip using a digital sliding caliper (MicroMak, USA). A digital scanner (Digital Persona, China) was utilized to capture the fingerprint pattern. Ridge count was determined by counting the ridges diagonally within a 5 mm × 5 mm area on the fingertip surface, located on the radial and ulnar sides of the distal regions of each finger. Results The proportion of the loop fingerprint pattern (ulnar and radial) on the left fourth finger of schizophrenia patients was significantly higher than that observed among the healthy volunteers. Generally, a smaller 2D:4D ratio was observed among male schizophrenia patients compared to female schizophrenia patients. The ridge counts in the second and fourth digits were significantly different among the patients compared to the healthy volunteers, except for the radial ridge counts on the left second digit. Conclusion Despite the varying degrees of association observed between the assessed anthropometric variables and schizophrenia risk, the diagnostic performance of each variable, as evaluated through ROC curve analysis, was consistently poor. Overall, these findings suggest that the sensitivity and specificity of these measurements in effectively distinguishing the disease remain inadequate. Further research is warranted to explore additional predictive factors and improve diagnostic accuracy in schizophrenia risk assessment.