Multivariate prediction of schizophrenia in adulthood utilizing childhood neurological data
Year: 2012
Type: dissertation
Abstract: Research has highlighted numerous benefits of identifying at-risk individuals before they develop schizophrenia. Longitudinal studies have elucidated a number of neurological deficits present in people with schizophrenia that can be measured premorbidly. Most of these studies, however, have suffered from methodological limitations, including only incorporating a single neurological variable, small sample size, and truncated follow-up assessment period. The objective of the current study was to examine the ability of multiple neurological variables to predict adult psychiatric status in high risk individuals and healthy controls. Data were derived from a longitudinal dataset of a large Danish cohort study begun in 1959, and included information on offspring of parents hospitalized with schizophrenia as well as age-matched controls. In adulthood, 32 offspring were diagnosed with a schizophrenia-spectrum disorder, 79 with a non-psychotic diagnosis and 133 with no diagnosable mental illness. The most accurate prediction model correctly classified 65.6% of schizophrenia-spectrum outcomes based on risk status and neurological data. Minor physical anomalies, a marker of pre-or perinatal complications, were the single most significant neurological predictor of schizophrenia-spectrum outcomes, followed by neuromotor dysfunction. Ocular alignment deficits, abnormal cerebral lateralization, and delayed developmental milestones contributed the least to predicting outcome diagnoses relative to other proxies of neurological dysfunction. Results are discussed with respect to the two-hit model of schizophrenia etiology.
Author: Shana Golembo Smith
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2743047299