20 March 2007. Deficits in attention and visual performance can identify individuals at risk for developing schizophrenia, according to a study by Mark F. Lenzenweger of the State University of New York at Binghamton and colleagues. The findings were published in the February issue of the Journal of Abnormal Psychology. Despite reports that people with schizophrenia and their biological relatives do poorly at paying attention and visually following moving objects, researchers had not previously taken the leap to using these dysfunctions to predict who is or is not at risk for the disease.
Attention and Eye-tracking Deficits as Endophenotypes
To speed the search for the genetic underpinnings of disease, some researchers look to endophenotypes, heritable disease markers that, although invisible to the naked eye, can be measured (see SRF Live Discussion led by Irving Gottesman). Endophenotypes may have simpler genetic determinants than diagnostic groups because they lie earlier in the gene-to-behavior pathway.
Lenzenweger and collaborators Geoff McLachlan at the University of Queensland, Australia, and Donald B. Rubin at Harvard University write that research supports regarding deficits in sustained attention (Wang et al., 2007) and visual tracking (Calkins and Iacono, 2000; Levy et al., 1994) as schizophrenia endophenotypes. Although most genetic models assume that people either have or lack schizophrenia risk, studies had not tested whether the structure underlying these abnormalities echoes this assumption.
To probe these markers’ latent structure, the investigators recruited community residents 18 to 45 years old for a study. They excluded those with a history of psychosis or of using antipsychotic medications. No participant tested psychotic.
The study measured sustained attention using the Continuous Performance Test—Identical Pairs Version, which asks participants to respond when two identical numbers appeared consecutively. In the eye-tracking task, people watched a red square move across a computer screen and pressed a button whenever they saw an “X” in the square change to an “O” or vice versa. The test gauged smooth pursuit eye movement, or the ability to follow a target, as well as saccades—small, jerky movements—to catch up with the target.
A statistical approach called finite mixture modeling showed that a two-group solution best fit the attention and eye-tracking data from 294 study participants. It estimated that 27 percent of subjects belonged in the schizotypic, or at-risk group, versus 73 percent not at risk. In contrast, the authors note, studies using psychometric measures have typically deemed about 10 to 15 percent of participants as at risk.
The analysis also estimated each individual’s probability of group membership. Splitting the sample based on those probabilities put 62 people in the presumed schizotypic group.
A Robust, Specific Finding
If the components truly reflect schizotypy, the investigators reasoned, those in the presumed schizotypic group should have more schizophrenia-related symptoms and relatives with the disorder than the other group. As expected, at-risk participants scored higher on reality distortion, negative symptoms, and disorganization, as well as overall symptoms, on the Schizotypal Personality Questionnaire. In addition, the participants reported that more of their biological relatives received treatment for schizophrenia.
Next, the researchers wondered whether the at-risk group might simply be more impaired in ways not specific to schizophrenia. Analyses ruled out between-group differences in education, intelligence, and family history of psychiatric disorders, including depression, bipolar disorder, anxiety disorders, alcohol or other drug abuse, and obsessive-compulsive disorder. “Thus, it appears that on the basis of the family history data, the schizotypic component was not merely tapping general psychosis-related liability in the subjects,” the authors note.
Lenzenweger and associates even tested whether a statistical technique called taxometric analysis would uphold the mixture modeling results. It not only confirmed the two groups, but also the estimate that 27 percent of the population belonged to the at-risk group, findings they called “relatively robust.”
“We stress that the 27 percent figure should not be taken to mean that 27 percent of the population is going to develop schizophrenia, as epidemiological data clearly do not support this,” the investigators write. More people may harbor liability for the illness than show schizophrenia-related psychopathology.
The researchers concluded that their findings support the all-or-none and tipping-point models of schizophrenia risk. They bring hope that clinicians might someday be able to spot schizophrenia-prone patients by checking their eye tracking and attention. Meanwhile, the approach used by Lenzenweger and colleagues may offer a more objective way to choose people for genomic study, using laboratory measures rather than clinical judgments.—Victoria L. Wilcox.
Lenzenweger MF, McLachlan G, Rubin DB. Resolving the latent structure of schizophrenia endophenotypes using expectation-maximum-based finite mixture modeling. J Abnormal Psychol. 2007;116(1):16-29. Abstract