Cognitive Processing in Monozygotic Twins Discordant for Chronic ...

Neuropsychology 2004, Vol. 18, No.2, 232-239

Copyright 2004 by the American Psychological Association 0894-4105/04/$12.00 DOl: 10.1037/0894-4105.18.2.232

Cognitive Processing in Monozygotic Twins Discordant for Chronic Fatigue Syndrome Roderick K. Mahurin

Keith H. Claypoole

University of Washington

University of Hawaii

Jack H. Goldberg

Lester Arguelles

University of Washington

University of Illinois at Chicago

Suzanne Ashton and Dedra Buchwald University of Washington

Twenty-one pairs of monozygotic twins discordant for chronic fatigue syndrome (CFS) and 21 matched healthy control (He) subjects were assessed with 5 untimed tests and 5 timed tests trom the computerbased N euroCoguitive Assessment Battery (R. K. Mahurin, 1993). Random effects regression showed no difference between CFS and healthy twins on any of the cognitive tests. Further, the twin groups did not differ trom the HC group on any content-dependent measure. In contrast, both sets of twins performed worse than the HC group on all speed-dependent tests except Finger Tapping. Selfrated fatigue and dysphoric mood were only weakly correlated with cognitive performance. These data point toward a shared genetic trait related to information processing that is manifest in the CFS context. The findings have implications for differentiating genetic and acquired vulnerability in the symptomatic expression of the disorder.

Chronic fatigue syndrome (CFS) is a medical disorder characterized by profound fatigue lasting at least 6 months, musculoskeletal pain, sleep disturbance, dysphoric mood, and impaired cognition (based on Center for Disease Control [CDC] criteria; Fukuda et a!., 1994; Komaroff et a!., 1996). Various theories on the pathophysiology of CFS have been proposed. Initially the prominence of infectious, neurocognitive, and psychological symptoms suggested a viral illness or psychiatric disorder; however, subsequent findings related to neuroendocrine, immunological, and autonomic dysfunction indicate a more multi determined etiology (Johnson, DeLuca, & Natelson, 1999; Komaroff & Buchwald, 1998). The associated somatic, cognitive, and mood changes of CFS can be debilitating and can have major effects on a patient's

Roderick K. Mahurin, Department of Radiology and Department of Psychiatry and Behavioral Sciences, University of Washington; Keith H. Claypoole, Department of Psychology, University of Hawaii; Jack H. Goldberg, Department of Epidemiology, University of Washington; Lester Arguelles, Division of Epidemiology-Biostatistics, University of Illinois at Chicago; Suzanne Ashton and Dedra Buchwald, Department of Medicine, University of Washington. Roderick K. Mahurin has disclosed a financial interest in the NeuroCognitive Assessment Battery and NeuroCognitive Assessment Systems. This research was supported in part by Grant UI9 AI38429 trom the National Institutes of Health to Dedra Buchwald. We thank the participants in the University of Washington Twin Registry for their cooperation, patience, and goodwill, and Leigh Sawyer, Program Officer, who is now at the National Institute of Allergy and Infectious Diseases, for her encouragement and support. We also acknowledge our advisory panel, whose advice and encouragement improved our scientific efforts. Correspondence concerning this article should be addressed to Roderick K. Mahurin, Department of Radiology, Box 357115, University of Washington, 1959 NE Pacific, Seattle, W A 98195-6465. E-mail: mahurin@ u.washington.edu

occupational, social, and physical well being (Anderson & Ferrans, 1997; Buchwald, Pearlman, Umali, Schmaling, & Katon, 1996). Cognitive impairment is reported by up to 80% of CFS patients (Tiersky, Johnson, Lange, Natelson, & DeLuca, 1997; Wessely, Chalder, Hirsch, Wallace, & Wright, 1996). Specific areas of cognitive dysfunction include attention (Krupp, Sliwinski, Masur, Friedberg, & Coyle, 1994; Michiels, Cluydts, & Fischler, 1998), reaction time (Marshall, Forstot, Callies, Peterson, & Schenck, 1997; Smith, Behan, Bell, Millar, & Bakheit, 1993), informationprocessing speed (DeLuca, Johnson, & Natelson, 1993; Marshall et a!., 1997), working memory (Dobbs, Dobbs, & Kiss, 2001; Marshall et a!., 1997), and psychomotor speed (DeLuca, Johnson, Beldowicz, & Natelson, 1995; Krupp et a!., 1994). Findings related to new learning and memory have been more inconsistent, but impairment was reported in many studies (DeLuca, Johnson, Ellis, & Natelson, 1997; Sandman, Barron, Nackoul, Goldstein, & Fidler, 1993) but not in others (Cope, Pernet, Kendall, & David, 1995). In contrast, general intellectual abilities, verbal fluency, concept formation, conceptual reasoning, and executive function generally are found to be intact in CFS patients (Michiels & Cluydts, 2001; Tiersky et a!., 1997). Although comorbid depression and anxiety are reported in up to 75% ofCFS patients (Manu, Lane, & Matthews, 1993; Wessely et a!., 1996), mood status generally has not been shown to correlate significantly with cognitive function (Schmaling, DiClementi, Cullum, & Jones, 1994; Vollmer-Conna et a!., 1997). In addition, cognitive function does not correlate well with either subjective or objective measures of fatigue severity (Joyce, Blumenthal, & Wessely, 1996; Krupp et a!., 1994; Marshall et a!., 1997). Although CFS has been associated with various environmental factors, recent studies have provided evidence that genes may play a role in expression of persistent fatigue in general (Hickie, Kirk, & Martin, 1999) and CFS in particular (Buchwald et a!., 2001).

COGNITION IN TWINS DISCORDANT FOR CHRONIC FATIGUE

Twin studies, in particular, represent an important methodology for separating environmental and genetic contributions into a broad range of cognitive processes (Bouchard, 1998; Brandt et aI., 1993; Finkel & McGue, 1993; McGue & Bouchard, 1998; Plomin, DeFries, McCleam, & Rutter, 1997; Segal, 1999). For example, a recent study showed that monozygotic twins discordant for CFS had a similar degree of postexercise cognitive impairment, as did their healthy cotwins. CFS twins' preexercise scores on the brief test battery were only slightly below that of the healthy twins (Claypoole et aI., 2001). A brain-imaging study of the same set of twins demonstrated no resting cerebral blood flow abnormalities in the CFS twins as compared with their healthy cotwins. These results were unaltered by adjustments for fitness level, depression, and mood before imaging (Lewis et aI., 2001). Another study of monozygotic and dizygotic twins discordant for CFS showed that although chronic fatigue and psychological distress are strongly associated, evidence is lacking for their genetic covariation (RoyByrne et aI., 2002). Beyond these studies, however, limited data is available regarding genetic influences on cognitive impairment in CFS patients. Thus, our study was designed to further explore possible genetic contributions to cognitive dysfunction in the disorder. We employed a cotwin control study of monozygotic twins in which one member of the pair met CDC-defined criteria for CFS and the other twin was healthy. Use of the comput_r-based NeuroCognitive Assessment Battery (referred to hereafter as the NeuroCog battery; Mahurin, 1993) allowed for objective assessment of cognitive abilities in the CFS twins and their healthy cotwins. The selected tests emphasized measurement of information-processing speed and efficiency, as well as verbal abilities, memory, and reasoning. Additionally, performance of the twin pairs was compared with reference data from healthy control (He) subjects equivalent in age, gender, and education. We hypothesized that CFS twins would perform worse than either their healthy cotwins or members of the HC group on speed-dependent, information-processing tests, but that the groups would not differ on content-dependent tests of verbal abilities, memory, and reasoning.

Method Participants Identification and recruitment of the twins. The twins described in this article were part of a larger study designed to examine genetic contributions to the medical and psychological aspects of CFS (Buchwald et a!., 2001). Twin pairs in which one or both members reported persistent fatigue were recruited for participation in a volunteer twin registry through patient support group newsletters (58%), clinicians or researchers familiar with CFS (11%), notices placed on electronic bulletin boards (15%), twin organizations and researchers (6%), relatives and friends (3%), and other sources (7%). Intake questionnaires were mailed to 600 individuals, and 426 (71 %) were returned. Complete data were available for both members of 193 twin pairs (386 individuals). The questionnaire collected information on demographics, zygosity, lifestyle, physical health conditions, the nature and extent of fatigue, and a checklist of CFS symptoms (Fukuda et a!., 1994). All twins provided written informed consent consistent with institutional guidelines. A complete description of the CFS Twin Registry can be found elsewhere (Buchwald et a!., 1999). Psychiatric disorders. The Diagnostic Interview Schedule (Version III-A [DIS]; Robins & Helzer, 1985) was administered by telephone to

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registry participants. The DIS was used to assess psychiatric symptoms in accord with the Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; American Psychiatric Association, 1987). Specific DIS modules included major depression, dysthymia, generalized anxiety, panic, agoraphobia, posttraumatic stress disorder, mania, bipolar affective disorders, schizophrenia, eating disorders, somatization, and substance abuse/ dependence. Physical and mental functional status was assessed by the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36; Ware, Kosinski, & Keller, 1994). Selection of the clinical sample. A restricted clinical sample was chosen from the twin registry for a 6-day in-person evaluation based on the intake questionnaire, the DIS, and additional telephone interviews and screening. Twins in this sample were required to (a) be at least 18 years old; (b) have been reared together; (c) be discordant for CFS (Le., one twin met the CDC criteria at the time of the evaluation and the other was healthy and had no history of chronic fatigue); (d) provide evidence of a recent negative mv test; (e) discontinue using alcohol, caffeine, and all medications known to affect sleep, cognition, immune, or inflanunatory function at least 2 weeks prior to and during the evaluation; (1) deny head trauma that was recurrent or accompanied by more than 5 min loss of consciousness; and (g) be able to travel at the same time to the testing center in Seattle. Of 193 twin pairs screened, 119 (62%) were discordant for 6 months of fatigue. Of these pairs, 67 (56%) were monozygotic and had complete data available; however, 14 fatigued twins had a CFS exclusionary psychiatric illnesses, 4 had a CFS exclusionary medical disorder, 1 had a body mass index over 45, and 9 did not meet CFS symptom criteria. In four pairs, the nonfatigued twin had a condition exclusionary for CFS, and six pairs were excluded for other reasons (e.g., pregnancy). Of the 29 remaining pairs in which one twin met criteria for CFS and their cotwin was healthy and denied chronic fatigue, 22 (76%) completed the study, 1 (3%) refused, 2 (7%) could not be scheduled, and 4 (14%) could not discontinue medications. Twenty-two sets of twins were ultimately selected for the clinical sample. There were no differences in physical or mental functional status (as measured by the SF-36) between the 22 twin pairs with CFS who traveled to the Seattle testing center and the 7 twin pairs who did not. One pair of twins did not complete testing for logistic reasons; therefore, data from the remaining 21 pairs of twins are presented here. The selected pairs were rigorously screened to ensure that the ill twin had CFS and the cotwin was healthy. The symptom checklist and diagnoses generated by the DIS were used to determine whether CFS criteria were met. All medical records for the previous 5 years were reviewed by an internist for exclusionary conditions. Health questions were resolved by telephone, physician contact, or laboratory tests performed prior to the evaluation. A psychologist and an infectious disease specialist independently reviewed the charts and approved the twins for participation. Just before the scheduled visit, screening questions were readministered to document that the status of the CFS and healthy twin had not changed. Zygosity was initially determined using validated self-report methods (Torgersen, 1979) and then confirmed by restriction fragment length polymorphisms. Following six probes, the probability of monozygosity can be presented with 99.9% certainty (Keith & Machin, 1997). The physical fitness of all twins was assessed during the intensive clinical examination using maximum oxygen consumption (VOz max) during a full exhaustive exercise challenge. Selection of the HC group. Members of the HC group were selected from the NeuroCog reference database to allow matching the twins on age, gender, and education. Data from these subjects were obtained under similar testing conditions as for the twins (Le., using comparable computer equipment and testing environment). A screening questionnaire was used to ensure that HC group members were free from psychiatric or medical disorders, including fatigue, depression, or other conditions that might affect cognitive function.

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Measures Tests from the NeuroCog battery were administered to all subjects. This previously validated, computer-administered test battery provides an integrated assessment of a subject's medical history, cognitive abilities, mood state, hand laterality, and healthrelated behaviors (Mahurin, 1993). All stimuli are presented on a computer monitor, and responses are made via the computer keyboard. Correlations of NeuroCog tests with conventional neuropsychological measures range from r = .72 to r = .98, and test-retest reliabilities range from r = .85 to r = .95 (Mahurin, 1993). NeuroCog, which incorporates reference data from over 500 previously screened healthy subjects, is the cognitive battery used in the International Consortium for Brain Mapping project (Mazziotta, Toga, Evans, Fox, & Lancaster, 1997). It also has been cross-referenced with clinical data from more than 300 patients in studies of fatigue, sleep loss, major depression, schizophrenia, epilepsy, multiple sclerosis, Alzheimer's disease, and Parkinson's disease (e.g., Mahurin, Bittner, & Heyer, 1997; Mahurin et aI., 1998; Mayberg et aI., 1997; Velligan & Bow-Thomas, 2000; Velligan et aI., 1997). Ten tests from the NeuroCog battery were divided a priori into (a) untimed, contentdependent tests that measure response accuracy (i.e., number of correct items); and (b) timed, speed-dependent tests that measure perceptual speed, response time, and information-processing efficiency. The test battery required approximately 1 hr for administration. Untimed tests. Five untimed tests were used. The Verbal Memory Test measures recent verbal memory. Twenty words are sequentially displayed on the computer monitor, followed by 40 words (20 original words and 20 new words). The subject selects whether each word was on the original list. The Visual Memory Test measures recent visual memory. Twenty colored geometric figures are sequentially displayed on the computer monitor, followed by 40 figures (20 original figures and 20 new figures). The subject selects whether each figure was shown in the first set of figures. The Vocabulary Test presents 40 pairs of words, half of which are synonyms and half are antonyms. The subject selects whether each pair of words means the same or opposite. The Logical Reasoning Test, which measures syntactical and logical verbal thinking, presents 16 true or false statements in the form "2 is less than 4" that randomly vary in the logic of the . statements (e.g., " . . . is not more than. . ."), order of presentation, and numbers used in the statements. The subject selects a response for each item. The Visual Conceptual Reasoning Test measures the ability to acquire, maintain, and shift abstract response sets. The subject views a series of designs to discover a recurring "rule" based on the size, shape, or color of the stimuli. This rule periodically shifts during the course of the test. The subject selects a response for each item and receives feedback as to the correctness of each response. Timed tests. Five timed tests were used. The Finger Tapping Test, which assesses fine motor speed and coordination, requires the subject to tap the index finger as rapidly as possible on the computer's space bar for 10 s. Two trials are averaged for the dominant and nondominant hand. The Simple Reaction Time Test measures simple attention, cognitive speed, and response time to a single predictable stimulus by requiring the subject to respond quickly to a sequence of randomly timed appearances of a horizontal arrow on the monitor. Median response times for the dominant and nondominant hand are averaged. The Choice Reaction Time Test is similar to the Simple Reaction Time Test in administration and scoring except that the subject must choose a right- or left-hand response depending on the direction of the arrow. It measures selective attention, speed of cognitive choice, and time of response to an unpredictable stimulus. The Stroop Color-Word Test (Interference subtest), which measures selective attention and response speed, presents a series of color names in which the letters of name are printed in a color that is incongruent with the name itself (e.g., red colored letters to spell the word blue; Stroop, 1935). The subject must quickly choose which of two names corresponds to the color that the letters are printed in, but not the name of the color. Two control tests of the same length and format also are administered, one that consists of matching colored bars to their names (Color subtest) and one that consists of

matching color names (printed in black) with corresponding names (Word subtest). Each response is timed, and the outcome measure is the total time for completion of the test. The Adaptive Rate Visual Processing Test assesses sustained attention, information-processing speed, and response time by sequentially presenting randomly varying checkerboard patterns on the computer monitor. The subject is instructed only to respond when a pattern appears twice in a row. The test uses an adaptive design in which the presentation rate slows down by .1-s increments each time the subject commits an error within a four-stimulus block, but it incrementally speeds up as the subject makes correct responses. The score represents percentage of correct responses at the fastest stimulus rate achieved by the subject. Mood scale. The Mood Scale is a self-report measure of a subject's affective state in the week prior to the assessment. Ten euthymic adjectives (e.g., relaxed, satisfied, etc.) and 10 dysthymic adjectives (e.g., depressed, worried, etc.) are presented in mixed order and rated on visual analog scales ranging from 0 (none) to 10 (very much). The sununed scores for each of these two sets of items comprise the Positive Mood scale and Negative Mood scale, respectively. The NeuroCog Mood Scale correlates higWy with the State-Trait Anxiety Scale (Spielberger, 1994; r = .82) and the Brief Symptom Inventory (Derogatis, 1993; r = .88) Within the Mood Scale are two separate items related to fatigue, one that asks how tired and the other how "energetic" the subject felt over the past week. A factor analysis of over 300 healthy subjects found that these 2 items were highly correlated (r = -.78) and represented an independent construct from the other Mood Scale items (Mahurin, 1993). Therefore, these two items were not included with the total Mood Scale score, but rather they were treated as a separate measure offatigue. Complete Mood Scale data were available only for the two groups of twins.

Data Analysis Due to the paired structure of twin data and the strong correlations within those pairs, a random effects regression model was employed to avoid the problems inherent in standard hypothesis testing of paired data (Hedeker & Gibbons, 1996). The specific model used included a random effect for the twin pair. Age, gender, and education were included as covariates in the regression model across groups; VO2 max data was also included for the twins; VO2 max was not tested in the HC group. The Tukey test was used to determine significance (adjusted a = .05) of multiple pairwise comparisons. Secondary correlational analyses of Mood Scale scores with cognitive scores were conducted within each group of twins. Significance of these correlations (a = .05) was adjusted for multiple comparisons within the untimed and timed tests. Between-group comparisons also were conducted on the Choice Reaction Time Test to Simple Reaction Time Test ratio and on the Stroop Color and Word subtests to Interference subtest ratio. These ratios represent additional cognitive-processing time required for the more complex conditions of each task (MacLeod, 1991).

Results Group Characteristics The mean age of the 21 twin pairs was 40.9 years, 19 pairs were female, and all were White. The CFS and healthy twins did not significantly differ in marital status or years of education, but the healthy twins were more likely to be employed,p < .006. The CFS twins had an average duration of illness of 7.2 years and a lower mean VO2 max than their cotwins, p < .05. Compared with the healthy twins, CFS twins scored higher on the Negative Mood scale, t(40) = 3.11, p < .003, and tired item, 1(40) = 5.02, p < .0001, and lower on the Positive Mood scale, t(40) = 2.97, P < .004, and energetic item, t(40) = 4.85, p < .0001. Mean age,


education, and gender of the HC group did not significantly differ from the two twin groups (see Table 1). NeuroCog Measures A random effects regression analysis revealed no significant difference between CFS and healthy twins on any of the untimed or timed tests from the NeuroCog battery (see Table 2). Additionally, neither the CFS twins nor the healthy twins significantly differed from the HC group on any of the untim_d tests in the battery. In contrast, however, both the CFS twins and their healthy cotwins perfonned significantly worse than the HC group on four of the five timed tests (Simple Reaction Time, Choice Reaction Time, Stroop Color-Word, and Adaptive Rate Visual Processing). Neither the CFS nor the healthy twins significantly differed from the HC group on the Finger Tapping Test. The ratio between Choice Reaction Time and Simple Reaction Time was similar across all three groups (CFS: M = lAO, SD = .22; healthy twin: M = 1.36, SD = .23; HC: M = 1.32, SD = .27), F(2, 60) = .51, P < .60. Likewise, no between-group differences were found for the ratio between the Stroop Interference and Color subtests (CFS: M = 1.54, SD = .34; healthy twin: M = 1.41, SD = .28; HC: M = 1045, SD = .35), F(2, 60) = .97, p < .39, or the Interference and Word subtests (CFS: M = 1.64, SD = .35; healthy twin: M = 1.47, SD = .27; HC: M = 1.61, SD = .36), F(3, 60) = 1.51,p < .23. Only two significant correlations were found between the cognitive tests and the mood and fatigue items. For the CFS twins, the energetic item showed a significant inverse correlation with Choice Reaction Time; for the healthy twins, the Negative Mood scale had a positive correlation with Vocabulary. Neither the tired item nor the Negative Mood scale was significantly correlated with any of the cognitive tests for either the CFS twins or the healthy twins (see Table 3). Discussion Consistent with our hypothesis, the CFS twins showed poorer perfonnance on four of the five speed-dependent, timed tests as Table I Characteristics of Twins With Chronic Fatigue Syndrome (CFS), Healthy Twins, and Healthy Control (HC) Group CFS twins Characteristic Demographic s Age (years) % Female Education (years) Fatigue and mooda VOz max (cc/kg/min)a Tired item Energetic item Positive Mood scaleb Negative Mood scalec a Data available only for twins.

N = 9 Negative Mood subscales.

C

* p < .05.

M

40.9 90.5 14.3 18.9 7.0 2.8 47.5 22.5

SD

Healthy twins M

!O.I 40.9 90.5 2.2 14.7 4.8 2.4 2.7 14.6 8.1

20.8 3.0* 6.8* 62.6* 13.8*

HC -

SD

M

SD

10.1

37.5 90.5 15.4

7.9

2.2

2.6

4.4 2.7 2.7 18.1 9.9

b N = 9 Positive Mood subscales.

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compared with the HC group. Unexpectedly, however, the healthy twins also scored lower than the HC group on the same four speeddependent tests. Neither the CFS twins nor the healthy twins differed significantly from the HC group on any of the contentdependent, untimed tests. The high similarity in perfonnance between the healthy twins and the CFS twins on timed tests suggests a genetically shared trait related to infonnation-processing abilities. Although both sets of twins perfonned significantly worse than the HC group on timed tests, neither the CFS nor the healthy twins differed from the HC group on any of the untimed tests. Because the twins did not differ on content-dependent tests, the difference between them and the HC group on the speed-dependent tests does not appear to be explained by a general cognitive factor. In addition, the three groups did not differ on the Vocabulary test score. This test correlates well with other vocabulary tests used as surrogate measures of intelligence (e.g., the Shipley Institute of Living Scale, r = .77; Zachary, Crumpton, & Spiegel, 1985), suggesting similar premorbid intellectual abilities across the groups (Lezak, 1995, p. 103). These data support previous findings of reduced infonnationprocessing speed and efficiency in CFS patients (DeLuca et a!., 1993; Marshall et a!., 1997; Michiels, de Gucht, Cluydts, & Fischler, 1999). Our data also are consistent with CFS studies showing overall nonnal functioning in other cognitive domains (Michiels & Cluydts, 2001; Tiersky et a!., 1997). A ratio analysis revealed that much of the slowing on the Choice Reaction Time Test condition can be accounted for by slowing in Simple Reaction Time, a pattern also noted in other studies (Marshall et a!., 1997; Smith et a!., 1993). Impainnent on the Stroop Color-Word Test also has been demonstrated in CFS patients (Ray, Phillips, & Weir, 1993; Smith et a!., 1993). Again, as we reported here, much of the slowing has been associated with simple decision-making processes and response speed, with little disproportionate effect (relative to healthy controls) in the Color-Word interference condition (Marshall et a!., 1997; Michiels et a!., 1998). Notably, Finger Tapping was the only timed measure that did not differ between the twins and the HC group. This finding is consistent with some (Gaudino, Coyle, & Krupp, 1997) but not all (Michiels et a!., 1996) other reports. However, unlike other timed tests in the battery, Finger Tapping involves relatively simple motor activity and places limited demands on infonnation-processing resources. In addition to affecting cognitive test perfonnance, the infonnation-processing difficulties in CFS patients have been associated with everyday functional disability (DeLuca et a!., 1993; Marshall et a!., 1997; Michiels et a!., 1999). This dysfunction may particularly be evident in tasks that require time-dependent, effortful, or divided allocation of mental resources (Joyce et a!., 1996; McDonald, David, Pelosi, & Mann, 1993; Wearden & Appleby, 1996). Although both sets of twins showed difficulties with information processing under test conditions, it is unclear why only the CFS twins appeared to experience cognitive problems that affected their everyday functioning. It has been suggested that the maintenance of CFS may be related to several pathophysiological processes, including covert encephalopathy or impaired physiological capability to respond to physical and mental stressors (Natelson & Lange, 2002). In addition, another study found that high body consciousness in combination with a high self-report of somatic symptoms was directly related to lower infonnation-processing

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Table 2 Cognitive Test Scores of Twins With Chronic Fatigue Syndrome (CFS), Healthy Twins, and Healthy Control (HC) Group CFS twins Measure

M

Healthy twins SD

M

SD

HC M

SD

Untimed tests Verbal Memory Visual Memory Vocabulary Logical Reasoning Conceptual Reasoning Timed tests Finger Tappinga Simple Reaction Timeb Choice Reaction Timeb Stroop Color-Wordc Adaptive Rate Visual processingd

33.6 31.9 34.1 14.4 41.3

2.7 3.6 2.2 1.2 6.1

34.5 32.1 35.1 14.5 42.1

3.4 3.7 2.5 1.7 4.4

0.19 329.0 450.3 86.1 0.13

0.03 63.0 71.7 17.1 0.17

0.18 328.6 439.3 81.8 0.10

0.02 59.0 64.1 16.9 0.15

35.9 33.1 33.9 15.0 41.4

4.2 4.7 8.1 1.5 6.0

0.17 277.3*.tt 367.3*.tt 70.1 *t 0.40**.ttt

0.03 46.0 104.3 25.5 0.21

Note. Random effects regression model adjusted for age, sex, education, and VOz max. a Taps/s. b Ms. C s. d Percentage of stimuli at fastest rate.

* p < .05, compared with CFS twins. ** p < .01, compared with CFS twins. t p < .05, compared with healthy twins. tt p < .01, compared with healthy twins. ttt p < .001, compared with healthy twins.

speed in CFS patients (van der Werf, de Vree, van Der Meer, & Bleijenberg, 2002). These findings support the role of psychological processes beyond possible cerebral dysfunction as alternative explanations for slowing of information processing in those with CFS. The information-processing difficulties noted in the present study may reach a threshold of impairment in the context of CFS, but they are not sufficient to cause difficulties in the healthy cotwins without CFS. The CFS twins rated themselves as having significantly greater dysphoric moods than did the healthy twin group. The greater degree of psychological distress in the CFS twins is consistent with previous reports (Marcel, Komaroff, Fagioli, Kornish, & Albert, 1996; Marshall et aI., 1996; Vollmer-Conna et aI., 1997). As expected, the CFS twins also had higher levels of self-reported fatigue on both of the fatigue items. Low correlations were found

between cognitive tests scores and either the mood or fatigue selfratings. The weak correlations of the mood and fatigue items with test scores are consistent with previous reports (Michiels & Cluydts, 2001; Schmaling et aI., 1994; Vollmer-Conna et aI., 1997) and indicate that the difference in cognitive performance between CFS and healthy twins was not due to mood or fatigue status. The presence of a genetic trait related to information-processing abilities is supported by many previous studies. Substantial heritability has been demonstrated for many specific cognitive processes, including attention (Myles-Worsley & Coon, 1997), multitask performance (Kee, Cherry, Neale, McBride, & Segal, 1998), perceptual speed (Finkel & Pedersen, 2000), and psychomotor speed (Swan, LaRue, Carmelli, Reed, & Fabsitz, 1992). In addition, previous studies have indicated there may be a genetic influence on specific information-processing abilities (Ho, Baker, &

Table 3 Correlations of Fatigue and Mood With Cognitive Test Scores for Twins With Chronic Fatigue Syndrome (CFS) and Healthy Twins Tired Measure

Energetic

Negative mood

Positive mood

CFS

Healthy

CFS

Healthy

CFS

Healthy

CFS

Healthy

.11 .13 .13 .19 .21

.24 .02 .44 .10 .19

-.21 -.22 -.23 -.05 -.13

-.24 -.20 -.34 -.10 -.40

.04 .05 .19 .08 .01

.16 .05 .52* .24 .31

-.13 -.07 -.09 -.01 -.07

-.16 -.07 -.32 -.10 -.28

.34 .30 .25 .03 .29

.19 .03 .01 .10 .37

-.10 -.37 -.52* -.32 -.19

-.01 -.03 -.13 -.05 -.24

.20 .27 .23 .03 .04

.19 .08 .06 .04 .19

-.13 -.40 -.40 -.07 -.05

-.02 -.18 -.31 -.01 -.13

Untimed tests Verbal Memory Visual Memory Vocabulary Logical Reasoning Conceptual Reasoning Timed tests Finger Tapping Simple Reaction Time Choice Reaction Time Stroop Color-Word Adaptive Rate Visual Processing

Note. Pearson product-moment correlations. * p < .05.

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Decker, 1988; Jensen, 1993; Petrill, Thompson, & Detterman, 1995; Rowe, 1994). A genetic component also has been shown for nerve conduction velocity and its correlation with general intelligence (Rijsdijk & Boomsma, 1997). The use of healthy cotwins as a control group for the CFS study is a significant strength. In addition to controlling for genetic variation between groups, a cotwin design, in which twins are raised together, partially accounts for factors related to early environment and upbringing that are difficult to account for in nontwin studies. Cotwin studies also are well suited to the investigation of illnesses of unclear etiology or for studies in which the appropriate comparison groups are unknown (Hrubec & Robinette, 1984). Nevertheless, our study has several limitations. Recruitment of the twins by advertisement and word of mouth resulted in a volunteer sample with potential ascertainment problems. However, the more desirable strategy of systematically identifying twins from a welldefined, population-based twin registry is not readily accomplished in the United States. Thus, how representative our twins were of either monozygotic twins in general or patients with CFS is difficult to determine even though the sociodemographic and clinical characteristics of our CFS subjects were similar to those in previous studies. A related issue is that because, as in many other studies of CFS, most of our subjects were women (Gunn, Connell, & Randall, 1993; Jason et aI., 1999), the results may not generalize to a CFS group consisting predominantly of men. With regard to the twin methodology itself, this study did not utilize healthyhealthy twins pairs as a secondary control group. Therefore, our data does not directly address the issue of whether twin status in itself may have contributed to the betweengroup differences. A similar test-based study of illnessdiscordant dizygotic, in addition to monozygotic, twins would also help clarify the genetic component of cognitive dysfunction in CFS patients. Lastly, we recognize the potential difficulties inherent in using cognitive data obtained from previously tested healthy subjects, rather than from a concurrently tested control group. However, the computer-based testing was highly standardized with comparable computer equipment and test conditions for all participants, thus diminishing threats to reliability normally associated with data collection at different sites (Spreen & Strauss, 1998). In summary, although CFS and healthy twins did not differ in cognitive performance, both sets of twins scored significantly lower than the HC group on timed tests involving informationprocessing speed and efficiency. In contrast, untimed tests that rely on content processing and reasoning abilities were performed by both sets of twins at a level similar to that of the HC group. Taken together, these data are consistent with previous reports of informationprocessing dysfunction in CFS patients. The findings also raise the possibility of a shared genetic trait related to specific informationprocessing abilities in twins discordant for CFS. However, cognitive difficulties in this area may not be fully expressed in absence of the chronic physiological or psychological stressors that characterize CFS. Further studies that include either healthyhealthy twins or dizygotic twins are needed to further characterize the genetic aggregation of symptoms related to this complex disorder.

References American Psychiatric Association. (1987). Diagnostic and statistical man ual of mental disorders (3rd ed., rev.). Washington, DC: Author. Anderson, J. S., & Ferrans, C. E. (1997). The quality oflife of persons with chronic fatigue syndrome. Journal of Nervous and Mental Disease, 185, 359-367. Bouchard, T. J. J. (1998). Genetic and environmental influences on adult intelligence and special mental abilities. Human Biology, 70, 253-275. Brandt, J., Welsh, K. A., Breitner, J. C., Folstein, M. F., Helms, M., & Christian, 1. C. (1993). Hereditary influences on cognitive functioning in older men: A study of 4,000 twin pairs. Archives of Neurology, 50, 599-603. Buchwald, D., Herrell, R., Ashton, S., Belcourt, M., Schmaling, K., & Goldberg, J. (1999). The Chronic Fatigue Twin Registry: Method of construction, composition, and zygosity assignment. Twin Research, 2, 203-211. Buchwald, D., Herrell, R., Ashton, S., Belcourt, M., Schmaling, K., Sullivan, P., et al. (2001). A twin study of chronic fatigue. Psychosomatic Medicine, 63, 936-943. Buchwald, D., Pearlman, T., Umali, J., Schmaling, K., & Katon, W. (1996). Functional status in patients with chronic fatigue syndrome, other fatiguing illnesses, and healthy individuals. American Journal of Medicine, 101, 364-370. Claypoole, K., Mahurin, R. K., Fischer, M. E., Goldberg, 1., Schrnaling, K. B., Schoene, R. B., et al. (2001). Cognitive compromise following exercise in monozygotic twins discordant for chronic fatigue syndrome: Fact or artifact? Applied Neuropsychology, 8, 31-40. Cope, H., Pernet, A., Kendall, B., & David, A. (1995). Cognitive functioning and magnetic resonance imaging in chronic fatigue. British Journal of Psychiatry, 167, 86-94. DeLuca, J., Johnson, S. K., Beldowicz, D., & Natelson, B. H. (1995). Neuropsychological impairments in chronic fatigue syndrome, multiple sclerosis, and depression. Journal of Neurology, Neurosurgery, and Psychiatry, 58, 38-43. DeLuca, J., Johnson, S. K., Ellis, S. P., & Natelson, B. H. (1997). Sudden versus gradual onset of chronic fatigue syndrome differentiates individuals on cognitive and psychiatric measures. Journal of Psychiatry Research, 31, 83-90. DeLuca, J., Johnson, S. K., & Natelson, 80 H. (1993). Information processing efficiency in chronic fatigue syndrome and multiple sclerosis. Archives of Neurology, 50, 301-304. Derogatis, L. R. (1993). Brief Symptom Inventory: Administration, Scoring, and Procedures Manual. Minneapolis: National Computer Systems, Inc. Dobbs, B. M., Dobbs, A. R., & Kiss, I. (2001). Working memory deficits associated with chronic fatigue syndrome. Journal of the International Neuropsychological Society, 7, 285-293. Finkel, D., & McGue, M. (1993). The origins of individual differences in memory among the elderly: A behavior genetic analysis. Psychology and Aging, 8, 527-537. Finkel, D., & Pedersen, N. L. (2000). Contribution of age, genes, and environment to the relationship between perceptual speed and cognitive ability. Psychology and Aging, 15, 56-64. Fukuda, K., Straus, S. E., Hickie, I., Sharpe, M. C., Dobbins, 1. G., & Komaroff, A. (1994). The chronic fatigue syndrome: A comprehensive approach to its definition and study. Annals of Internal Medicine, 121, 953-959. Gaudino, E. A., Coyle, P. K., & Krupp, L. B. (1997). Post-Lyme syndrome and chronic fatigue syndrome: Neuropsychiatric similarities and differences. Archives of Neurology, 54, 1372-1376. Gunn, W. 1., Connell, D. B., & Randall, B. (1993). Epidemiology of chronic fatigue syndrome: The Centers for Disease Control study. Ciba Foundation Symposium, 173, 83-101.

238

MAHURIN ET AL.

Hedeker, D., & Gibbons, R. D. (1996). MIXREG: A computer program for mixedeffects regression analysis with autocorrelated errors. Computer Methods and Programs in Biomedicine, 49, 229-252. Hickie, I., Kirk, K., & Martin, N. (1999). Unique genetic and environmental determinants of prolonged fatigue: A twin study. Psychological Medicine, 29, 259268. Ho, H. Z., Baker, L. A., & Decker, S. N. (1988). Covariation between intelligence and speed of cognitive processing: Genetic and environmental influences. Behavioral Genetics, 18, 247-261. Hrubec, Z., & Robinette, C. D. (1984). The study of human twins in medical research. New England of Medicine, 310, 435-441. Jason, L. A., Richman, J. A., Rademaker, A. W., Jordan, K. M., Plioplys, A. V., Taylor, R. R., et al. (1999). A community-based study of chronic fatigue syndrome. Archives of Internal Medicine, 159, 2129-2137. Jensen, A. R. (1993). Spearman's g: Links between psychometrics and biology. Annals of the New York Academy of Sciences, 702, 103-129. Johnson, S. K., DeLuca, 1., & Natelson, B. H. (1999). Chronic fatigue syndrome: Reviewing the research findings. Annals of Behavioral Med icine, 21, 258-271. Joyce, E., Blumenthal, S., & Wessely, S. (1996). Memory, attention, and executive function in chronic fatigue syndrome. Journal of Neurology, Neurosurgery, and Psychiatry, 60, 495-503. Kee, D. W., Cherry, B. 1., Neale, P. L., McBride, D. M., & Segal, N. L. (1998). Multitask analysis of cerebral hemisphere specialization in monozygotic twins discordant for handedness. Neuropsychology, 12, 468-478. Keith, L., & Machin, G. (1997). Zygosity testing: Current status and evolving issues. Journal of Reproductive Medicine, 42, 699-707. Komaroff, A. L., & Buchwald, D. S. (1998). Chronic fatigue syndrome: An update. In C. H. Coggins, E. W. Hancock, & L. J. Levitt (Eds.), Annual review of medicine (Vol. 49, pp. 1-13). Palo Alto, CA: Annual Reviews. Komaroff, A. L., Fagioli, L. R., Geiger, A. M., Doolittle, T. H., Lee, 1., Kornish, R. 1., et al. (1996). An examination of the working case definition of chronic fatigue syndrome. American Journal of Medicine, 100, 56-64. Krupp, L. B., Sliwinski, M., Masur, D. M., Friedberg, F., & Coyle, P. K. (1994). Cognitive functioning and depression in patients with chronic fatigue syndrome and multiple sclerosis. Archives of Neurology, 51, 705-710. Lewis, D. H., Mayberg, H. S., Fischer, M. E., Goldberg, 1., Ashton, S., & Graham, M. M. (2001). Monozygotic twins discordant for chronic fatigue syndrome: Regional cerebral blood flow SPECT. Radiology, 219, 766-773. Lezak, M. D. (1995). Neuropsychological assessment (3rd ed.). New York: Oxford University Press. MacLeod, C. M. (1991). Half a century of research on the Stroop effect: An integrative review. Psychological Bulletin, 109, 163-203. Mahurin, R. K. (1993). Manual: NeuroCognitive Assessment Battery. Seattle, W A: NeuroCognitive Assessment Systems. Mahurin, R. K., Bittner, A. C., & Heyer, N. J. (1997). Brain imaging and human performance: Fatigue and occupational exposure applications. In W. Karwowski (Ed.), Advances in occupational ergonomics and safety (Vol. 2, pp. 125-130). Amsterdam: IDS Press. Mahurin, R. K., Bittner, A. C., Heyer, N. 1., Mayberg, H. S., Brannan, S. K., & McGinnis, S. (1998). Using resting brain patterns to address ergonomic questions: A neuroimaging and NeuroCog test battery based illustration. In S. Kumar (Ed.), Advances in occupational ergonomics and safety (Vol. 3, pp. 236-239). Amsterdam: IOS Press. Manu, P., Lane, T. 1., & Matthews, D. A. (1993). Chronic fatigue and chronic fatigue syndrome: Clinical epidemiology and aetiological classification. Ciba Foundation Symposium, 173, 23-31. Marcel, B., Komaroff, A. L., Fagioli, L. R., Kornish, R. J., & Albert, M. S. (1996). Cognitive deficits in patients with chronic fatigue syndrome. Biological Psychiatry, 40, 535-541.

Marshall, P. S., Forstot, M., Callies, A., Peterson, P. K., & Schenck, C. H. (1997). Cognitive slowing and working memory difficulties in chronic fatigue syndrome. Psychosomatic Medicine, 59, 58-66. Marshall, P. S., Watson, D., Steinberg, P., Cornblatt, B., Peterson, P. K., Callies, A., et al. (1996). An assessment of cognitive function and mood in chronic fatigue syndrome. Biological Psychiatry, 39, 199-206. Mayberg, H. S., Brannan, S. K., Mahurin, R. K., Jerabek, P. A., Brickman, J. S., Tekell, J. L., et al. (1997). Cingulate function in depression: A potential predictor oftreatrnent response. Neuroreport, 8, 1057-1061. Mazziotta, J. C., Toga, A., Evans, A., Fox, P., & Lancaster, 1. (1997). Brain maps: Linking the present to the future. In R. S. J. Frackowiak, K. J. Friston, C. D. Frith, R. J. Dolan, & 1. C. Mazziotta (Eds.), Human brain function (pp. 429-466). New York: Academic Press. McDonald, E., David, A. S., Pelosi, A. J., & Mann, A. H. (1993). Chronic fatigue in primary care attenders. Psychological Medicine, 23, 987-998. McGue, M., & Bouchard, T. J. J. (1998). Genetic and environmental influences of human behavioral differences. Annual Review of Neuro science, 21, 1-24. Michiels, V., & Cluydts, R. (2001). Neuropsychological functioning in chronic fatigue syndrome: A review. Acta Psychiatrica Scandinavia, 103, 84-93. Michiels, V., Cluydts, R., & FiscWer, B. (1998). Attention and verbal learning in patients with chronic fatigue syndrome. Journal of the International Neuropsychological Society, 4, 456-466. Michiels, V., Cluydts, R., FiscWer, 8., Hoffmann, G., LeBon, 0., & De Meirleir, K. (1996). Cognitive functioning in patients with chronic fatigue syndrome. Journal of Clinical and Experimental Neuropsychology, 18, 666-677. Michiels, V., de Gucht, V., Cluydts, R., & Fischler, B. (1999). Attention and information processing efficiency in patients with chronic fatigue syndrome. Journal of Clinical and Experimental Neuropsychology, 21, 709-729. Myles-Worsley, M., & Coon, H. (1997). Genetic and developmental factors in spontaneous selective attention: A study of normal twins. Psychiatry Research, 71, 163-174. Natelson, B. H., & Lange, G. (2002). A status report on chronic fatigue syndrome. Environmental Health Perspective, I100Suppi. 4), 673-677. Petrin, S. A., Thompson, L. A., & Detterman, D. K. (1995). The genetic and environmental variance underlying elementary cognitive tasks. Be havioral Genetics, 25, 199-209. Plomin, R., DeFries, 1. C., McClearn, G. E., & Rutter, 1. (1997). Behav ioral genetics. New York: Freeman. Ray, C., Phillips, L., & Weir, W. R. C. (1993). Quality of attention in chronic fatigue syndrome: Subjective reports of everyday attention and cognitive difficulty, and performance on tasks of focused attention. British Journal of Clinical Psychology, 32, 357-364. Rijsdijk, F. V., & Boomsma, D. I. (1997). Genetic mediation of the correlation between peripheral nerve conduction velocity and IQ. Behavioral Genetics, 27, 87-98. Robins, L. N., & Helzer, J. E. (1985). Diagnostic Interview Schedule (DIS): Version Ill-R. St. Louis, MO: Washington University. Rowe, D. D. (1994). The limits offamily influence. New York: Guilford Press. Roy-Byrne, P., Afari, N., Ashton, S., Fischer, M., Goldberg, J., & Buch wald, D. (2002). Chronic fatigue and anxiety/depression: A twin study. British Journal of Psychiatry, 180, 29-34. Sandman, C. A., Barron, J. L., Nackoul, K., Goldstein, J., & Fidler, F. (1993). Memory deficits associated with chronic fatigue immune dys function syndrome. Biological Psychiatry, 33, 618-623. Schmaling, K. 8., DiClementi, J. D., Cullum, C. M., & Jones, J. F. (1994). Cognitive functioning in chronic fatigue syndrome and depression: A preliminary comparison. Psychomsomatic Medicine, 56, 383-388. Segal, N. L. (1999). Entwined lives. New York: Dutton.


Smith, A. P., Behan, P.O., Bell, W., Millar, K., & Bakheit, M. (1993). Behavioural problems associated with the chronic fatigue syndrome. British Journal of Psychology, 84, 411-423. Spielberger, C. D. (1994). State-Trait Anxiety Inventory. Redwood City, CA: Mind Garden. Spreen, 0., & Strauss, E. (1998). A compendium of neuropsychological tests: Administration, norms, and commentary (2nd ed.). New York: Oxford University Press. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643-662. Swan, G. E., LaRue, A., Carmelli, D., Reed, T. E., & Fabsitz, R. R. (1992). Decline in cognitive performance in aging twins: Heritability and biobehavioral predictors from the National Heart, Lung, and Blood Institute Twin Study. Archives of Neurology, 49, 476-481. Tiersky, L. A., Johnson, S. K., Lange, G., Natelson, B. H., & DeLuca, J. (1997). Neuropsychology of chronic fatigue syndrome: A critical review. Journal of Clinical and Experimental Neuropsychology, 19, 560586. Torgersen, S. (1979). The determination of twin zygosity by means of a mailed questionnaire. Acta Geneticae Medicae et Gemellogiae, 28, 225236. van der Werf, S. P., de Vree, B., van Der Meer, J. W., & Bleijenberg, G. (2002). The relations among body consciousness, somatic symptom report, and information processing speed in chronic fatigue syndrome. Neuropsychiatry Neuropsychology and Behavioral Neurology, 15,2-9. Velligan, D. 1., & Bow-Thomas, C. C. (2000). Two case studies of cognitive adaptation training for outpatients with schizophrenia. Psychiatric Services, 51, 2528.

239

Velligan, D. 1., Mahurin, R. K., Diamond, P. L., Hazleton, B. C., Eckert, S. L., & Miller, A. L. (1997). The functional significance of symptomatology and cognitive function in schizophrenia. Schizophrenia Research, 25, 21-31. Volhner-Conna, U., Wakefield, D., Lloyd, A., Hickie, 1., Lemon, 1., Bird, K. D., & Westbrook, R. F. (1997). Cognitive deficits inpatients suffering from chronic fatigue syndrome, acute infective illness or depression. British Journal of Psychiatry, 171, 377-381. Ware, J. E., Kosinski, M., & Keller, S. D. (1994). SF-36 Physical and Mental Health Summary Scales: A user's manual. Boston: The Health Institute. Wearden, A. 1., & Appleby, L. (1996). Research on cognitive complaints and cognitive functioning in patients with chronic fatigue syndrome (CFS): What conclusions can we draw? Journal of Psychosomatic Research, 41, 197-211. Wessely, S., Chalder, T., Hirsch, S., Wallace, P., & Wright, D. (1996). Psychological symptoms, somatic symptoms, and psychiatric disorder in chronic fatigue and chronic fatigue syndrome: A prospective study in the primary care setting. American Journal of Psychiatry, 153, 1050-1059. Zachary, R. A., Crumpton, E., & Spiegel, D. E. (1985). Estimating W AIS-R IQ from the Shipley Institute of Living Scale. Journal of Clinical Psychology, 41, 532-540.

Received May 2, 2002 Revision received January 3, 2003 Accepted March 31, 2003 .