Genetic and environmental influences on sleep quality in middle-aged men: a twin study.

Poor sleep quality is a risk factor for a number of cognitive and physiological age-related disorders. Identifying factors underlying sleep quality are important in understanding the etiology of these age-related health disorders. We investigated the extent to which genes and the environment contribute to subjective sleep quality in middle-aged male twins using the classical twin design. We used the Pittsburgh Sleep Quality Index to measure sleep quality in 1218 middle-aged twin men from the Vietnam Era Twin Study of Aging (mean age = 55.4 years; range 51-60; 339 monozygotic twin pairs, 257 dizygotic twin pairs, 26 unpaired twins). The mean PSQI global score was 5.6 [SD = 3.6; range 0-20]. Based on univariate twin models, 34% of variability in the global PSQI score was due to additive genetic effects (heritability) and 66% was attributed to individual-specific environmental factors. Common environment did not contribute to the variability. Similarly, the heritability of poor sleep-a dichotomous measure based on the cut-off of global PSQI>5-was 31%, with no contribution of the common environment. Heritability of six of the seven PSQI component scores (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, and daytime dysfunction) ranged from 0.15 to 0.31, whereas no genetic influences contributed to the use of sleeping medication. Additive genetic influences contribute to approximately one-third of the variability of global subjective sleep quality. Our results in middle-aged men constitute a first step towards examination of the genetic relationship between sleep and other facets of aging.

Factor analysis of neurocognitive tests in a large sample of schizophrenic probands, their siblings, and healthy controls.

Large batteries of neuropsychological tests are typically necessary to identify cognitive deficits in schizophrenia and routinely examine multiple cognitive processes, with many tests often yielding more than one measure of interest. This study investigates the feasibility of a partial solution to the problem of multiple comparisons: the use of factor analysis to reduce the number of phenotypic variables and to better understand the underlying cognitive architecture in schizophrenia. Using a principle components analysis followed by a varimax rotation, we identified factor structures for schizophrenic patients (n=99), their unaffected siblings (n=167), and control subjects (n=131), both separately and as a composite group. Exploratory factor analysis of the full sample yielded a 7-factor model that included verbal memory, working memory, visual memory, IQ/speed/fluency, executive function, attention and digit span. A confirmatory factor analysis (CFA) with maximum likelihood estimation revealed that the 7-factor model fit observed data from the three groups adequately. Since we identified a factor structure representative of all groups that reduced 24 original variables to 7 variables of interest, factor analysis was useful in reducing the complexity of large batteries of cognitive measures to more manageable numbers of phenotypic variables. Furthermore, these findings provide the first confirmation that cognitive structure is comparable in family members of schizophrenia patients, as well as in patients themselves and controls.