Chronic Fatigue Syndrome: Maloney EM

Nater UM, Lin JM, Maloney EM, Jones JF, Tian H, Boneva RS, Raison CL, Reeves WC, Heim C. · Chronic Viral Diseases Branch, National Center for Zoonotic, Vector-borne and Enteric Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA. · Psychosom Med. · Pubmed #19414619 No free full text.

Abstract: OBJECTIVE: To compare the prevalence of psychiatric disorders in persons with chronic fatigue syndrome (CFS) identified from the general population and a chronically ill group of people presenting with subsyndromic CFS-like illness ("insufficient symptoms or fatigue" (ISF)). Previous studies in CFS patients from primary and tertiary care clinics have found high rates of psychiatric disturbance, but this may reflect referral bias rather than true patterns of comorbidity with CFS. METHODS: We used random digit dialing to identify unwell individuals. A detailed telephone interview identified those with CFS-like illness. These individuals participated in a 1-day clinical evaluation to confirm CFS or ISF status. We identified 113 cases of CFS and 264 persons with ISF. To identify current and lifetime psychiatric disorders, participants completed the Structured Clinical Interview for DSM-IV. RESULTS: Sixty-four persons (57%) with CFS had at least one current psychiatric diagnosis, in contrast to 118 persons (45%) with ISF. One hundred one persons (89%) with CFS had at least one lifetime psychiatric diagnosis compared with 208 persons (79%) with ISF. Of note, only 11 persons (9.8%) with CFS and 25 persons (9.5%) with ISF reported having seen a mental healthcare specialist during the past 6 months. CONCLUSIONS: Our findings indicate that current and lifetime psychiatric disorders commonly accompany CFS in the general population. Most CFS cases with comorbid psychiatric conditions had not sought appropriate help during the past 6 months. These results demonstrate an urgent need to address psychiatric disorders in the clinical care of CFS cases.

Maloney EM, Boneva R, Nater UM, Reeves WC. · Chronic Viral Diseases Branch, National Center for Zoonotic, Vector-borne and Enteric Diseases, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A-15, Atlanta, GA 30333, USA. · Psychosom Med. · Pubmed #19414615 No free full text.

Abstract: OBJECTIVE: To confirm the association of chronic fatigue syndrome (CFS) with high allostatic load (AL) level, examine the association of subsyndromal CFS with AL level, and investigate the effect of depression on these relationships and the association of AL with functional impairment, fatigue, symptom severity, fatigue duration, and type of CFS onset. AL represents the cumulative physiologic effect of demands to adapt to stress. METHODS: Population-based case-control study of 83 persons with CFS, 202 persons with insufficient symptoms or fatigue for CFS (ISF), and 109 well controls living in Georgia. Unconditional logistic regression was used to generate odds ratios (ORs) as measures of the association of AL with CFS. RESULTS: Relative to well controls, each 1-point increase in allostatic load index (ALI) was associated with a 26% increase in likelihood of having CFS (OR(adjusted) = 1.26, 95% Confidence Interval (CI) = 1.00, 1.59). This association remained in the presence and absence of depression (OR(adjusted) = 1.35, CI = 1.07, 1.72; OR(adjusted) = 1.35, CI = 1.10, 1.65). Compared with the ISF group, each 1-point increase in ALI was associated with a 10% increase in likelihood of having CFS (OR(adjusted) = 1.10, CI = 0.93, 1.31). Among persons with CFS, the duration of fatigue was inversely correlated with ALI (r = -.26, p = .047). CONCLUSIONS: Compared with well controls, persons with CFS were significantly more likely to have a high AL. AL increased in a gradient across well, ISF, and CFS groups.

Smith AK, Maloney EM, Falkenberg VR, Dimulescu I, Rajeevan MS. · Division of Viral and Rickettsial Diseases, National Center for Zoonotic, Vector-Borne and Enteric Diseases, Centers for Disease Control and Prevention, 1600 Clifton Road, MSG41, Atlanta, GA 30333, USA. · Psychoneuroendocrinology. · Pubmed #19081678 No free full text.

Abstract: Allostatic load (AL) is a theoretical framework that describes the cumulative physiologic effects of adaptation to change or stress throughout the lifespan. AL is operationalized by a composite index of multiple biomarkers. Accordingly, genes, behavior and environment contribute to AL. To determine if individual differences in AL may be influenced by inherent genetic variation, we calculated an allostatic load index (ALI) for 182 Caucasian subjects derived from a population-based study of chronic fatigue syndrome. Nearly 65% of the subjects in this study sample reported fatiguing illness. ALI was calculated based on 11 measures representing metabolic, cardiovascular, inflammatory, hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system (SNS) activities. Subjects were dichotomized into high (ALI > or = 3) or low (ALI < 3) AL groups, and the association between high AL and 129 polymorphisms in 32 genes related to the HPA axis, neurotransmission, inflammation, cardiovascular and metabolic functions were evaluated. Polymorphisms in angiotensin-1 converting enzyme (ACE), corticotropin-releasing hormone receptor 1 (CRHR1), and serotonin receptors (HTR3A and HTR4) were associated with AL (p=0.0007-0.0486), but only one polymorphism, rs4968591, in ACE remained significant after correction for multiple comparisons. The T allele of ACE rs4968591 was more common in subjects with high AL (67.5%) than in subjects with low AL (49.3%) (p=0.0007), and this effect appeared independent of age, sex, body mass index and fatigue status. Additionally, high interleukin-6 (IL-6; p(trend)=0.04), and C-reactive protein (CRP; p(trend)=0.01) levels, as well as low urinary cortisol levels in females (p=0.03) were associated with the T allele, which may result in allele-specific binding of the transcription factor, E2F1. Our results suggest a role for ACE in the bidirectional communication between the central nervous and immune systems in response to stress. Further studies will be needed (a) to replicate the association between AL and ACE polymorphisms in population studies designed to differentiate the effects of sex, age and racial/ethnic background, (b) to evaluate the effect of allele-specific binding of E2F1 at rs4968591, and (c) to examine the role of ACE in the co-regulation of CRP, IL-6 and cortisol.

Boneva RS, Decker MJ, Maloney EM, Lin JM, Jones JF, Helgason HG, Heim CM, Rye DB, Reeves WC. · Chronic Viral Diseases Branch, National Center for Zoonotic, Vector-borne and Enteric Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA. · Auton Neurosci. · Pubmed #17851136 No free full text.

Abstract: Autonomic nervous system (ANS) dysfunction has been suggested in patients with chronic fatigue syndrome (CFS). In this study, we sought to determine whether increased heart rate (HR) and reduced heart rate variability (HRV) parameters observed in CFS patients during wakefulness persist during sleep. To this end, we compared heart rate (HR) and HRV as indicators of ANS function in CFS subjects and non-fatigued (NF) controls in a population-based, case-control study. Thirty subjects with CFS and 38 NF controls, matched for age-, sex- and body mass index, were eligible for analysis. Main outcome measures included mean RR interval (RRI), HR, and HRV parameters derived from overnight ECG. Plasma aldosterone and norepinephrine levels, medicines with cardiovascular effect, and reported physical activity were examined as covariates. General Linear Models were used to assess significance of associations and adjust for potential confounders. Compared to controls, CFS cases had significantly higher mean HR (71.4 vs 64.8 bpm), with a shorter mean RRI [840.4 (85.3) vs 925.4(97.8) ms] (p<0.0004, each), and reduced low frequency (LF), very low frequency (VLF), and total power (TP) of HRV (p<0.02, all). CFS cases had significantly lower plasma aldosterone (p<0.05), and tended to have higher plasma norepinephrine levels. HR correlated weakly with plasma norepinephrine (r=0.23, p=0.05) and moderately with vitality and fatigue scores (r=-0.49 and 0.46, respectively, p<0.0001). Limitation in moderate physical activity was strongly associated with increased HR and decreased HRV. Nevertheless, among 42 subjects with similar physical activity limitations, CFS cases still had higher HR (71.8 bpm) than respective controls (64.9 bpm), p=0.023, suggesting that reduced physical activity could not fully explain CFS-associated differences in HR and HRV. After adjusting for potential confounders case-control differences in HR and TP remained significant (p<0.05). Conclusion: the presence of increased HR and reduced HRV in CFS during sleep coupled with higher norepinephrine levels and lower plasma aldosterone suggest a state of sympathetic ANS predominance and neuroendocrine alterations. Future research on the underlying pathophysiologic mechanisms of the association is needed.

Jones JF, Maloney EM, Boneva RS, Jones AB, Reeves WC. · Division of Viral and Rickettsial Diseases, Coordinating Center for Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA. · BMC Complement Altern Med. · Pubmed #17459162 links to  free full text

Abstract: BACKGROUND: Chronic fatiguing illnesses, including chronic fatigue syndrome (CFS), pose a diagnostic and therapeutic challenge. Previous clinical reports addressed the utilization of health care provided to patients with CFS by a variety of practitioners with other than allopathic training, but did not examine the spectrum of complementary and alternative medicine (CAM) therapies used. This study was designed to measure CAM therapy use by persons with fatiguing illnesses in the United States population. METHODS: During a random-digit dialing survey to estimate the prevalence of CFS-like illness in urban and rural populations from different geographic regions of the United States, we queried the utilization of CAM including manipulation or body-based therapies, alternative medical systems, mind-body, biologically-based, and energy modalities. RESULTS: Four hundred forty fatigued and 444 non-fatigued persons from 2,728 households completed screening. Fatigued subjects included 53 persons with prolonged fatigue, 338 with chronic fatigue, and 49 with CFS-like illness. Mind-body therapy (primarily personal prayer and prayer by others) was the most frequently used CAM across all groups. Among women, there was a significant trend of increasing overall CAM use across all subgroups (p-trend = 0.003). All categories of CAM use were associated with significantly poorer physical health scores, and all but one (alternative medicine systems) were associated with significantly poorer mental health scores. People with CFS-like illness were significantly more likely to use body-based therapy (chiropractic and massage) than non-fatigued participants (OR = 2.52, CI = 1.32, 4.82). Use of body-based therapies increased significantly in a linear trend across subgroups of non-fatigued, prolonged fatigued, chronic fatigued, and CFS-like subjects (p-trend = 0.002). People with chronic fatigue were also significantly more likely to use body-based therapy (OR = 1.52, CI = 1.07, 2.16) and mind-body (excluding prayer) therapy than non-fatigued participants (OR = 1.73, CI = 1.20 - 2.48). CONCLUSION: Utilization of CAM was common in fatiguing illnesses, and was largely accounted for by the presence of underlying conditions and poor physical and mental health. Compared to non-fatigued persons, those with CFS-like illness or chronic fatigue were most likely to use body-based and mind-body therapies. These observations have important implications for provider education programs and development of intervention strategies for CFS.

Reeves WC, Heim C, Maloney EM, Youngblood LS, Unger ER, Decker MJ, Jones JF, Rye DB. · Viral Exanthems & Herpesvirus Branch, Division of Viral & Rickettsial Diseases, National Center for Infectious Diseases, Centers for Disease Control & Prevention, Atlanta, GA, USA. · BMC Neurol. · Pubmed #17109739 links to  free full text

Abstract: BACKGROUND: The etiology and pathophysiology of chronic fatigue syndrome (CFS) remain inchoate. Attempts to elucidate the pathophysiology must consider sleep physiology, as unrefreshing sleep is the most commonly reported of the 8 case-defining symptoms of CFS. Although published studies have consistently reported inefficient sleep and documented a variable occurrence of previously undiagnosed primary sleep disorders, they have not identified characteristic disturbances in sleep architecture or a distinctive pattern of polysomnographic abnormalities associated with CFS. METHODS: This study recruited CFS cases and non-fatigued controls from a population based study of CFS in Wichita, Kansas. Participants spent two nights in the research unit of a local hospital and underwent overnight polysomnographic and daytime multiple sleep latency testing in order to characterize sleep architecture. RESULTS: Approximately 18% of persons with CFS and 7% of asymptomatic controls were diagnosed with severe primary sleep disorders and were excluded from further analysis. These rates were not significantly different. Persons with CFS had a significantly higher mean frequency of obstructive apnea per hour (p = .003); however, the difference was not clinically meaningful. Other characteristics of sleep architecture did not differ between persons with CFS and controls. CONCLUSION: Although disordered breathing during sleep may be associated with CFS, this study generally did not provide evidence that altered sleep architecture is a critical factor in CFS. Future studies should further scrutinize the relationship between subjective sleep quality relative to objective polysomnographic measures.

Goertzel BN, Pennachin C, de Souza Coelho L, Maloney EM, Jones JF, Gurbaxani B. · Virginia Tech, National Capital Region, Arlington, Virginia, USA. · Pharmacogenomics. · Pubmed #16610958 No free full text.

Abstract: OBJECTIVES: To further explore the relationship between chronic fatigue syndrome (CFS) and allostatic load (AL), we conducted a computational analysis involving 43 patients with CFS and 60 nonfatigued, healthy controls (NF) enrolled in a population-based case-control study in Wichita (KS, USA). We used traditional biostatistical methods to measure the association of high AL to standardized measures of physical and mental functioning, disability, fatigue and general symptom severity. We also used nonlinear regression technology embedded in machine learning algorithms to learn equations predicting various CFS symptoms based on the individual components of the allostatic load index (ALI). METHODS: An ALI was computed for all study participants using available laboratory and clinical data on metabolic, cardiovascular and hypothalamic-pituitary-adrenal (HPA) axis factors. Physical and mental functioning/impairment was measured using the Medical Outcomes Study 36-item Short Form Health Survey (SF-36); current fatigue was measured using the 20-item multidimensional fatigue inventory (MFI); frequency and intensity of symptoms was measured using the 19-item symptom inventory (SI). Genetic programming, a nonlinear regression technique, was used to learn an ensemble of different predictive equations rather just than a single one. Statistical analysis was based on the calculation of the percentage of equations in the ensemble that utilized each input variable, producing a measure of the 'utility' of the variable for the predictive problem at hand. Traditional biostatistics methods include the median and Wilcoxon tests for comparing the median levels of subscale scores obtained on the SF-36, the MFI and the SI summary score. RESULTS: Among CFS patients, but not controls, a high level of AL was significantly associated with lower median values (indicating worse health) of bodily pain, physical functioning and general symptom frequency/intensity. Using genetic programming, the ALI was determined to be a better predictor of these three health measures than any subcombination of ALI components among cases, but not controls.

Goertzel BN, Pennachin C, de Souza Coelho L, Gurbaxani B, Maloney EM, Jones JF. · Virginia Tech, National Capital Region, Arlington, VA, USA. · Pharmacogenomics. · Pubmed #16610957 No free full text.

Abstract: OBJECTIVE: This paper asks whether the presence of chronic fatigue syndrome (CFS) can be more accurately predicted from single nucleotide polymorphism (SNP) profiles than would occur by chance. METHODS: Specifically, given SNP profiles for 43 CFS patients, together with 58 controls, we used an enumerative search to identify an ensemble of conjunctive rules that predict whether a patient has CFS. RESULTS: The accuracy of the rules reached 76.3%, with the highest accuracy rules yielding 49 true negatives, 15 false negatives, 28 true positives and nine false positives (odds ratio [OR] 8.94, p < 0.0001). Analysis of the SNPs used most frequently in the overall ensemble of rules gave rise to a list of 'most important SNPs', which was not identical to the list of 'most differentiating SNPs' that one would calculate via studying each SNP independently. The top three genes containing the SNPs accounting for the highest accumulated importances were neuronal tryptophan hydroxylase (TPH2), catechol-O-methyltransferase (COMT) and nuclear receptor subfamily 3, group C, member 1 glucocorticoid receptor (NR3C1). CONCLUSION: The fact that only 28 out of several million possible SNPs predict whether a person has CFS with 76% accuracy indicates that CFS has a genetic component that may help to explain some aspects of the illness.

Maloney EM, Gurbaxani BM, Jones JF, de Souza Coelho L, Pennachin C, Goertzel BN. · Centers for Disease Control and Prevention, 1600 Clifton Road, MS A-15, Atlanta, GA 30333, USA. · Pharmacogenomics. · Pubmed #16610956 No free full text.

Abstract: STUDY POPULATION: We examined the relationship between chronic fatigue syndrome (CFS) and allostatic load in a population-based, case-control study of 43 CFS patients and 60 nonfatigued, healthy controls from Wichita, KS, USA. METHODS: An allostatic load index was computed for all study participants using available laboratory and clinical data, according to a standard algorithm for allostatic load. Logistic regression analysis was used to compute odds ratios (ORs) as estimates of relative risk in models that included adjustment for matching factors and education; 95% confidence intervals (CIs) were computed to estimate the precision of the ORs. RESULTS: CFS patients were 1.9-times more likely to have a high allostatic load index than controls (95% CI = 0.75, 4.75) after adjusting for education level, in addition to matching factors. The strength of this association increased in a linear trend across categories of low, medium and high levels of allostatic load (p = 0.06). CONCLUSION: CFS was associated with a high level of allostatic load. The three allostatic load components that best discriminated cases from controls were waist:hip ratio, aldosterone and urinary cortisol.

Gurbaxani BM, Jones JF, Goertzel BN, Maloney EM. · 1Centers for Disease Control and Prevention, 600 Clifton Road, MS A-15, Atlanta, GA 30333, USA. · Pharmacogenomics. · Pubmed #16610955 No free full text.

Abstract: OBJECTIVES: To provide a mathematical introduction to the Wichita (KS, USA) clinical dataset, which is all of the nongenetic data (no microarray or single nucleotide polymorphism data) from the 2-day clinical evaluation, and show the preliminary findings and limitations, of popular, matrix algebra-based data mining techniques. METHODS: An initial matrix of 440 variables by 227 human subjects was reduced to 183 variables by 164 subjects. Variables were excluded that strongly correlated with chronic fatigue syndrome (CFS) case classification by design (for example, the multidimensional fatigue inventory [MFI] data), that were otherwise self reporting in nature and also tended to correlate strongly with CFS classification, or were sparse or nonvarying between case and control. Subjects were excluded if they did not clearly fall into well-defined CFS classifications, had comorbid depression with melancholic features, or other medical or psychiatric exclusions. The popular data mining techniques, principle components analysis (PCA) and linear discriminant analysis (LDA), were used to determine how well the data separated into groups. Two different feature selection methods helped identify the most discriminating parameters. RESULTS: Although purely biological features (variables) were found to separate CFS cases from controls, including many allostatic load and sleep-related variables, most parameters were not statistically significant individually. However, biological correlates of CFS, such as heart rate and heart rate variability, require further investigation. CONCLUSIONS: Feature selection of a limited number of variables from the purely biological dataset produced better separation between groups than a PCA of the entire dataset. Feature selection highlighted the importance of many of the allostatic load variables studied in more detail by Maloney and colleagues in this issue [1] , as well as some sleep-related variables. Nonetheless, matrix linear algebra-based data mining approaches appeared to be of limited utility when compared with more sophisticated nonlinear analyses on richer data types, such as those found in Maloney and colleagues [1] and Goertzel and colleagues [2] in this issue.