Objective
The purpose of the study was to evaluate quantitative sleep electroencephalogram (EEG) frequencies in monozygotic twins discordant for chronic fatigue syndrome.
Methods
Thirteen pairs of female twins underwent polysomnography. During the first night, they adapted to the sleep laboratory, and during the second night, their baseline sleep was assessed. Visual stage scoring was conducted on sleep electroencephalographic records according to standard criteria, and power spectral analysis was used to quantify delta through beta frequency bands, processed in 6-s blocks. Data were averaged across sleep stage within each twin and coded for sleep stage and the presence or absence of chronic fatigue syndrome (CFS). A completely within-subjects repeated measure multivariate analysis of variance evaluated twin pairs by frequency band by sleep stage interactions and simple effects. The relationship between alpha and delta EEG was also assessed across twin pairs.
Results
No significant differences in spectral power in any frequency band were found between those with CFS and their nonfatigued cotwins. Phasic alpha activity, coupled with delta was noted in five subjects with CFS but was also present in 4/5 healthy twins, indicating this finding likely reflects genetic influences on the sleep electroencephalogram rather than disease-specific sleep pathology.
Conclusions
The genetic influences on sleep polysomnography and microarchitecture appear to be stronger than the disease influence of chronic fatigue syndrome, despite greater subjective sleep complaint among the CFS twins. EEG techniques that focus on short duration events or paradigms that probe sleep regulation may provide a better description of sleep abnormalities in CFS.
Chronic fatigue syndrome (CFS) is characterized by profound fatigue lasting at least 6 months accompanied by disturbances of sleep, cognition, mood, musculoskeletal pain, and other symptoms [1]. Insomnia and insufficient, nonrestorative sleep are among the most common and disabling symptoms [2], [3], [4], [5] and [6]. Clinic-based studies have found that patients with CFS often have poor sleep efficiency [5], [7], [8], [9], [10], [11] and [12] and, occasionally, intrinsic sleep disorders such as obstructive sleep apnea [2], [5], [7], [8] and [13]. These studies, however, have methodological differences and limitations including the absence of comparison groups [5], [8], [11] and [13], failure to include laboratory sleep data [6] and [14], use of in-home sleep studies [10] and [14], reporting of clinical sleep disorders without data on sleep architecture [2], and the inclusion of only a single laboratory night [2], [5], [8], [10], [12] and [13]. Small but rigorously conducted studies have not provided strong evidence for striking abnormalities in sleep architecture among most patients with CFS [15] and [16]. Thus, methodological differences, the lack of control for many genetic and environmental factors, and the inherent limitations of standard electroencephalogram (EEG) likely contribute to the inability to reproducibly detect differences in sleep microarchitecture between CFS and healthy control groups.
Quantitative EEG analysis procedures may be a more sensitive metric for evaluating sleep abnormalities in clinical populations than traditional manual sleep stage scoring [17] and [18]. One study of sleep clinic patients with chronic fatigue demonstrated increased “slow delta” power and a higher cyclic alternating pattern (CAP) rate in the CFS group [19]. Increased alpha activity during sleep also has been inconsistently observed in fibromyalgia [20], [21], [22], [23], [24] and [25], a disorder closely related to CFS that is characterized by chronic, unexplained, widespread pain [26]. The limited studies of quantitative sleep EEG in CFS or other related disorders provided a strong rationale for the present study.
Cotwin control studies offer a powerful alternative to traditional approaches that compare CFS patients to healthy or depressed individuals, while controlling for genetic and numerous environmental factors [27]. This research design is particularly valuable in studies of sleep where genetic factors contribute substantially to sleep architecture [28], the number of data points generated is large, and the range of values observed in normal individuals is wide. We therefore compared the power spectral analysis of sleep EEGs between twins discordant for CFS to answer these questions: does sleep architecture differ between twins with CFS and their nonaffected cotwins and is there greater prevalence of alpha-activity phase-locked with delta in the twins with CFS?
