The overall case-control effect size that we observed, 0.62, was somewhat lower than that reported in meta-analyses . Since the patient sample was older than the control sample and age significantly affected P300, the patient control difference was attenuated somewhat by inclusion of age as a covariate. The effect size was almost certainly also lowered by our conservative data strategy, which likely excluded a number of subjects – primarily patients – with negligible but real P300 responses. This moderately large effect is, nevertheless, well within the expected distribution of published studies. Although we observed a significant difference across test sites, this did not reflect differences in data quality, methodology, or experimental rigor. Rather it reflected differences in the stratification of the samples across sites, as this relates to clinical and socio-demographic confounds or modifiers. In patients, site differences were entirely explained by differences in the level of positive symptomatology. Although the P300 deficit is traditionally thought of as being immune to changes in patients’ clinical status , it should probably be considered as more of a relatively stable deficit. It clearly does not normalize with treatment, even when symptoms dramatically improve. However, it still exhibits modulation over time in association with positive symptoms . Indeed, it is this ability to reflect increasing positive symptomatology that underlies the emerging utility of P300 as a predictive biomarker for imminent prodromal conversion to psychosis . Except for MMSE and UPSA-B,growing indoor cannabis global indices of cognitive ability and real-world functional capacity, no other clinical measures were associated with P300, indicating that the association with positive symptoms is relatively specific.
Since these patients were all clinically stable outpatients on stable medication regiments, differences in positive symptomatology presumably reflected relatively stable trait-like differences on this dimension of illness severity. P300 may therefore be an endophenotype that is especially informative regarding the genetic basis of positive symptoms. The associations with MMSE and UPSA-B highlight the utility of the P300 as a sensitive physiological index of differences in brain function, even within a relatively homogeneous clinical sample. The magnitude of the P300 response has long been considered a broad indicator of “cognitive fitness” and, more specifically, of the ability to appropriately process and respond to task-salient environmental inputs — i.e., to correctly detect a signal within noise. It is thought to require intact attentional and working memory capacities , and to reflect complex neural processes of temporal and spatial integration across multiple brain regions . It is not surprising, therefore, that the P300 would correlate with other measures of cognitive and functional capacity. A similar association between P300 amplitude and MMSE has been reported previously in chronic Alzheimer’s disease patients and, acutely, in uremic patients undergoing dialysis , where the two measures showed a correlated improvement, as well, following treatment. There have been no prior studies reporting a relationship between P300 and specific measures of functional capacity, including UPSA-B, either in schizophrenia patients or other clinical samples. However, this association is entirely consistent with the relationship between P300 and cognition. Prior studies examining the relationship between neurocognitive and functional deficits have routinely found that cognitive ability, specifically working memory, is the strongest predictor of schizophrenia patients’ real-world functional capacity . Indeed, in our own data, we observed a similar robust correlation between MMSE and UPSA-B. These associations support the utility of P300 amplitude as a potential biomarker for predictive risk and treatment studies.
However, they also emphasize the relatively non-specific nature of the measure. This was evident, as well, in the control sample data. In these otherwise healthy subjects, P300 amplitude was affected by smoking, race and, as one mediator of the race effect, prior history of substance abuse or dependence. Previous studies have shown that nicotine reduces P300 , yet – despite the well-known propensity of schizophrenia patients to smoke – there has been virtually no consideration of the effect of smoking on the auditory P300 in patients. We observed no parallel effect of nicotine in patients, presumably because their ERPs were already suppressed. This is consistent with a recent small study of healthy subjects administered with intravenous ketamine. Ketamine induced schizophrenia-like symptoms and attenuated the auditory target P300 response, but this was unaffected by co-administration of nicotine vs. placebo . Similarly, reduced P300 has been associated with the use of stimulants , opioids and cannabis . Yet, again, we saw no effect of prior substance use on P300 in the schizophrenia patients. This mirrors what was recently reported in a study examining the effects of cannabis in prodromal subjects considered to be at ultra-high risk for developing psychosis. In this sample, those with a history of cannabis use were indistinguishable from those without. However, among the otherwise-healthy controls, those who used cannabis had reduced P300 responses that were indistinguishable from those of the prodromal sample . The impact of substance abuse on the African-American sample may reflect differences in the specific character and/or quantity of substance use within the different racial groupings, which are not captured by a simple dichotomous categorization. Similarly, the residual effects of race, independent of past substance use, could reflect the impact of other psychosocial stressors in the different racial communities. Unfortunately, we have no objective measures of either of stressful life events or physiological markers of stress to test this hypothesis.The fact that modulating factors such as nicotine and substance abuse can differential affect controls, but not patients, raises an important cautionary note about how to interpret study results, potential false negative findings, and what constitutes the best comparison sample for genetic or biomarker studies. A common recommended strategy is to recruit control subjects who are similar to the clinical sample on various modulating factors and co-morbid conditions. The results of this study would seem to temper that recommendation, at least for P300. It suggests that, in matching the samples, individual and group differences may be attenuated for reasons other than psychosis. Consequently, genetic associations with the endophenotype may be obscured and the ability of the measure to predict transition to psychosis may be weakened.
However, the broad utility of P300 as a robust marker for large multi-site studies is confirmed, along with important associations with both positive symptoms and decreased cognitive and functional capacity.KNOWLEDGE ABOUT A person’s family history of a disorder can be useful in both clinical and research settings . For clinicians, knowing if relatives ever evidenced a Mendelian dominant or recessive genetic disorder can help identify early signs of the condition and might contribute to patients’ decisions about having children. Knowledge of a familial complex genetically influenced disorder is also useful, but less informative because each relevant gene probably contributes to only a small proportion of the risk and environment is likely to play a major role . Directly relevant to the current report, knowledge of a subject’s FH can also help researchers select individuals or families on which to focus efforts in order to control costs and maximize the usefulness of the data gathered . There is general agreement that the most accurate FH comes from the Family Study Method that uses personal interviews with all available first- and second-degree relatives . But that approach has downsides including high cost, the time,indoor cannabis growing and effort needed to gather the information from all available relatives, as well as the bias of nonrandom missing data resulting from lack of information on relatives who have died, those too ill to be interviewed, individuals who cannot be located, as well as those who refuse to participate. In an alternate approach, the Family History Method , data can be gathered relatively quickly and at lower cost. Here, relatively simple instruments ask informants about demography and clinical conditions regarding multiple relatives . FHM downsides include inconsistencies in an individual’s reports over time and disagreements among relatives regarding the familial problems . The most salient drawback is the relatively low sensitivity of FHMs . The specific disorder studied relates to FHM sensitivity where obvious medical conditions, are likely to have high FHM sensitivities , but identifying relatives’ psychiatric disorders have lower sensitivities . Regarding substance use disorders , more accurate FHM reports are seen for smoking tobacco with less impressive sensitivities for misuse of alcohol and illicit drugs. Regardless of the condition studied, specificities are often over 90%. The average sensitivities of FHM studies of familial alcohol problems, including AUDs, usually range between 30 and >50% . An example of more promising FHM results involved correlations of 0.70 between college students’ alcohol problems of their parents and those parents self-report using variations of the Short Michigan Alcohol Screening Test . However, offspring missed alcohol use disorder -like problems in 17% of fathers and 50% of mothers. In addition, the parents’ self-reports were not validated by repeated structured interviews over time, the analyses focused on problem patterns for the parents rather than AUDs, and no information was available regarding the performance of individual AUD criteria.
Another study evaluated twin pair concordance regarding their father’s alcohol problems , but the paper focused on agreement among the off- spring reports and did not include DSM AUD criteria. In addition to the condition being studied, other characteristics are also associated with higher FHM sensitivities. These include more severe alcohol histories in the subjects ; informants with conditions that are similar to the subject’s problems ; a subject for whom AUDs ran in their family ; a close genetic relationship between informant and subject ; a focus on more observable criteria such as having ever been in treatment ; the use of less demanding criteria for the subject’s condition ; and when multiple informants are used . The relationships of informants’ and subjects’ demographic characteristics to the accuracy of the informant’s reports vary across studies. Regarding sex, some investigations noted that female subjects with alcohol problems were more likely to be correctly identified than male subjects, although the sex of the informant might have less impact on the accuracy of the reports . However, other studies noted that male offspring might more accurately report alcohol problems in their mothers and female offspring be more accurate in reporting alcohol problems in their fathers . The importance to sensitivity of an informant’s education is also not clear, with one study reporting a fivefold lower accuracy for an informant’s report about a parent’s smoking history for informants with a college education compared to those with less than high school completion . The informant’s age was reported to have little relationship to the validity of their report in some studies , but Pape and colleagues reported a twofold higher odds ratio for correct offspring reports for older informants. The relationships of informants’ and subjects’ demographic characteristics to the accuracy of substance use histories using the FHM require additional study. If demography is closely related to FHM sensitivity, it could be easier to identify which informant is likely to give the most accurate FH. Our every 5-year evaluation over 35 years for members of 2 generations of the San Diego Prospective Study offers an opportunity to expand information regarding relationships of demography and other characteristics to the accuracy of reports of parental alcohol-related problems by offspring. The SDPS incorporates many of the better-established characteristics related to higher accuracy of informants’ FHM reports. These include using standardized personal interviews with subjects and informants, relatively severe alcohol problems and high levels of alcohol intake in subjects, a first degree genetic relationship between informants and subjects, high rates of alcohol problems in both generations, and the focus on relatively broad alcohol problems for fathers that do not require that full AUD criteria be met for the informant’s report to be considered valid. The data reported here were used to evaluate 3 hypotheses. These included the following: higher levels of education in the offspring with the potential greater understanding of human behavior and a greater awareness of problems in their environment will be associated with higher rates of recognition of paternal alcohol problems; for potential reasons similar to the impact of higher levels of education, older informants will more accurately report a father’s alcohol problems; and female offspring will more accurately report their father’s AUDs, as noted in some prior studies.After additional Human Subject’s Protection Committee review, beginning in 1988 100% of the original 453 SDPS probands were located, 99% of whom agreed to participate in the follow-up protocol.