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Publications: Molecular Epidemiology

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Vernon SD, Whistler T, Aslakson E, Rajeevan M, Reeves WC
Challenges for Molecular Profiling of Chronic Fatigue Syndrome
Pharmacogenomics 7:211-218, 2006

Summary

Abstract

Successful utilization of genomics data to identify pharmacological targets for complex illnesses such as chronic fatigue syndrome (CFS) will require systematic integration of epidemiological, clinical, and laboratory data. Sound epidemiological and clinical data is necessary to obtain information to calculate the associations and magnitude of risk from laboratory measurements.

Population-based studies have clearly documented the medical and public health problem CFS poses. Between 400,000–900,000 adults in the USA suffer from the illness; most have been ill for at least 5 years; they are functionally impaired to a similar extent as persons with multiple sclerosis and heart disease; a quarter of them are unemployed or receiving disability allowance; and, the average family in whom a member has CFS foregoes US$20,000 annually in lost earnings and wages (half the median US household income). As no characteristic physical signs or diagnostic laboratory abnormalities herald CFS, diagnosis depends on the evaluation of self-reported symptoms and ruling out medical or psychiatric conditions that could explain the illness. There is no specific treatment, so management aims to relieve symptoms. Prognosis is poor as only one third of primary care CFS patients improve by 1 year and, of those referred to secondary care, less than 10% return to premorbid functioning. As a result of this, medical management of CFS is extraordinarily difficult and time consuming; one report documented that CFS patients undergo an average of 13 primary care and five referral care visits.

CFS consists of incapacitating, persistent or relapsing fatigue of at least 6-months duration that is not relieved by rest and is accompanied by a characteristic (but nonspecific) symptom complex, which includes unusual postexertional malaise, unrefreshing sleep, impaired memory/concentration, headache, muscle pain, joint pain, sore throat and tender lymph nodes. Studies have consistently identified several risk factors for CFS, in particular, age, female sex, physical and emotional stressors, and certain personality and behavioral traits.
This understanding of the epidemiology of CFS has not been paralleled by achievements elucidating its pathophysiology or etiology. Despite more than 3000 peer-reviewed scientific publications, no characteristic laboratory marker for CFS has been consistently identified. Research has instead demonstrated what CFS is not attributed to – a muscle disorder, a retroviral infection, an autoimmune disease, or a psychiatric disorder. Alterations in the immune system and hypothalamic–pituitary–adrenal (HPA) axis function have been well documented, however it is still unclear whether these alterations are primary or secondary factors in CFS.

There are at least three reasons why biomarker discovery and the elucidation of CFS pathophysiology has proved challenging. First is the fact that CFS is a diagnosis of exclusion, defined by self reported symptoms. Recently an International CFS Study Group recommended the use of standardized instruments to objectively measure the major domains of the illness, specifically:

These standardized measures can be used to identify (and stratify) patients with CFS based on the extent of their disability, the duration and severity of fatigue and the duration and severity of their symptoms.

The second reason why biomarker discovery and the elucidation of CFS pathophysiology has proved challenging involves study design and the study subjects themselves. Numerous, elegant population-based studies have been conducted in order to estimate the prevalence of CFS. The next step is to enroll subjects, identified in these population-based studies, into those aimed at biomarker discovery. However, because of the high cost and challenging logistics involved in enrolling individuals identified in population-based studies for subsequent biomarker studies, many investigators rely on studying case and control subjects that are convenient. These types of convenience samples lead to difficulties in determining associations, relating risk back to the general population and replicating the findings. In addition, studies of patients identified from clinics suffer from referral bias, which cannot be measured and is likely to be quite large for illnesses such as CFS. For example, only half of individuals identified to suffer from CFS in the general population of Chicago (IL, USA) had consulted a physician for their illness and only 16% of people with CFS identified in the general population of Wichita (KS, USA) had been diagnosed and treated for CFS by a physician.

The third reason is that CFS is an illness with alterations in complex body wide systems of homeostasis. New, high-throughput technology exists to detect and characterize every genetic polymorphism, gene transcript and protein, but is this sufficient to enable the discovery of biomarkers for detection and surveillance, risk assessment, and therapeutic intervention of CFS? If these technological advances are coupled with a sound study design and integrated along with careful and appropriate epidemiological and clinical measurements, optimal sample collection and powerful computational tools, then sensitive and specific biomarkers can be identified..
Objectively measured and empirically evaluated biomarkers, at the least, identify unique groups of people and serve as indicators of normal vs. abnormal biological processes . Biomarkers can be used to stratify and diagnose patient populations and may serve as indicators of drug efficacy or toxicity. The most pressing need for the CFS research community is biomarkers that can be used to objectively identify and stratify CFS cases in order to customize intervention. The following sections describe some of the successes and challenges of molecular profiling in the discovery of biomarkers for the diagnosis, therapeutic intervention and ultimately prevention of CFS.

CFS Molecular Epidemiology Program

The CFS Molecular Epidemiology Program combines epidemiology with powerful molecular and genomic technologies to search for markers and subtypes of CFS and acquire an understanding of the underlying biologic correlates. The aim of the Molecular Epidemiology Program is to characterize CFS at a systems biology level by using genetics, proteomics, physiology, and population-based epidemiology. To date, we have focused on gene expression profiling, which is a method for examining the activity or transcription of genes by analyzing the messenger RNA in cells. Gene expression is altered by many factors, including cell differentiation, metabolic states, and disease status. By comparing the gene expression profiles between samples, characteristic differences can be identified. These differences, known as differential gene expression, can point to markers for diagnosis or altered physiologic pathways.

Microarrays allow for simultaneous detection and evaluation of hundreds to thousands of expressed genes, an approach essential for understanding biological pathways and processes. Most gene expression studies have focused on samples derived from cells or tissues with a known lesion. However, diseases such as CFS have no known lesion to sample and evaluate. Thus, it is critical to rigorously standardize all aspects of laboratory testing. The majority of CDC work to date has focused on standardization of assays.

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Page last modified on Oct. 27, 2008

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