RESEARCH ARTICLE Neuropsychiatric Genetics
The search for Peripheral Biomarkers for Major Depression: Benefiting from Successes in the Biology of Smoking Robert Philibert,1,2* Helen M. Gunter,2,3 and Iris-Tatjana Kolassa2,4 1
Department of Psychiatry, University of Iowa, Iowa City, Iowa
2
Zukunftskolleg, University of Konstanz, Konstanz, Germany Department of Biology, University of Konstanz, Konstanz, Germany
3 4
Clinical & Biological Psychology, University of Ulm, Ulm, Germany
Manuscript Received: 17 September 2013; Manuscript Accepted: 29 January 2014
The search for robust, clinically useful markers for major depression (MD) has been relatively unproductive. This is unfortunate because MD is one of the largest socio-economic challenges for much of the world and the development of reliable biomarkers for MD could aid in the prevention or treatment of this common syndrome. In this editorial, we compare the approaches taken in the search for biomarkers for MD to that of the more successful searches for biomarkers for tobacco use, and identify several substantive barriers. We suggest that many of the existing clinical repositories used in these biomarkers searches for MD may be of limited value. We conclude that in the future greater attention should be given to the clinical definitions, characterization of confounding environmental factors and age of subjects included in studies. Ó 2014 Wiley Periodicals, Inc.
Key words: substance use; major depression; biomarker
INTRODUCTION The search for clinically useful biological markers (biomarkers) for major depression (MD) has, to date, been relatively unproductive [Schmidt et al., 2011; Sim and Ingelman-Sundberg, 2011]. Despite the investment of considerable intellectual and economic resources, many feel that we have exhausted all possible leads, and that the scientific quest to identify biomarkers for MD is nearing an unsuccessful conclusion. This is an unfortunate conclusion because in the 21st century, MD has escalated from an intellectual curiosity to a profound socioeconomic challenge. Over the lifetime, MD affects nearly one in four Americans. From an economic perspective, by 2030 it will be the 2nd largest economic burden worldwide [Sasayama et al., 2011]. From the personal perspective, suicide, an all-too-frequent outcome, is the 10th leading cause of death in the United States [Murphy et al., 2012]. In response to that challenge, a number of pharmacological and cognitive/behavioral treatment paradigms for MD have been developed and empirically validated in research settings. However,
Ó 2014 Wiley Periodicals, Inc.
How to Cite this Article: Philibert R, Gunter HM, Kolassa I-T. 2014. The search for peripheral biomarkers for major depression: Benefiting from successes in the biology of smoking. Am J Med Genet Part B 165B:230–234.
when these treatments are generalized to the wider population, their effectiveness rapidly diminishes––sometimes to the point where their effects are not discernible [Kirsch et al., 2008]. In the current healthcare environment, which stresses the necessity of using evidence-based treatment paradigms in order to be reimbursed, the near absence of generalizable effects for some of these treatments raises questions with respect to the validity of the diagnostic criteria and the need to reimburse psychiatric treatment. The development of biomarkers for MD, or more adroitly focusing on MD-treatment responsiveness would address many Conflict of interest: Dr. Philibert is supported by R01MH080789 and R21DA034457. He is the Chief Scientific Officer for Behavioral Diagnostics whose product focus is epigenetic markers for substance use and is both an owner and a royalty recipient of granted and pending patents for the use of DNA methylation to diagnose substance use. Grant sponsor: Deutsche Forschungsgemeinschaft and the Zukunftskolleg at the University of Konstanz; Grant sponsor: European commission (Competitiveness and Innovation Framework Programme, CIP); Grant sponsor: German Research Foundation (DFG); Grant sponsor: Federal Ministry of Education and Research (BMBF); Grant sponsor: Heidelberg Academy of Sciences (WIN Kolleg). Correspondence to: Robert Philibert, M.D., Ph.D., Department of Psychiatry, University of Iowa, Room 2-126b, Psychiatry Research/MEB, Iowa City, Iowa 52242. E-mail: [email protected] Article first published online in Wiley Online Library (wileyonlinelibrary.com): 4 March 2014 DOI 10.1002/ajmg.b.32227
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PHILIBERT ET AL. of these issues. In particular, the development of the latter would improve the health care of millions of individuals by; (1) helping to identify sub-syndromal cases that would otherwise fester untreated, (2) allowing for the exclusion from treatment of those individuals who are unlikely to benefit from treatment, (3) contributing to efforts to better prevent MD, and (4) providing a tool to better understand the phenomenology and biology of non-treatment responsive MD. Therefore, there is a substantial rationale for MD biomarker development. Conceivably, useful biomarkers for MD could be developed through genetic, epigenetic, serological, or imaging approaches. Unfortunately, at the current time, several lengthy, authoritative reviews of the subject have concluded that none of these approaches have resulted in the generation of clinically useful biomarkers for either categorical MD or MD-treatment response. These reviews also do not suggest a set of mechanisms through which this knowledge deficit can be rectified. In this editorial, using valuable insights gained from the searches for biomarkers for tobacco use disorders, we identified three crucial roadblocks and suggest a set of concrete recommendations to improve current efforts to identify peripheral biomarkers for MD.
BARRIERS TO THE DEVELOPMENT OF BIOMARKERS FOR MD AND THEIR POTENTIAL WORKAROUNDS Unreliable Diagnosis Although there have been a large number of potential hurdles for the development of robust biomarkers for MD, perhaps the most severe has been the unreliability of MD diagnosis. To a certain extent, this is a circular argument and the clinical diagnosis of MD has rested largely on self-report data. Unfortunately, a large number of behavioral studies have shown that self-report data for affective symptoms can be unreliable [Wittkampf et al., 2007]. As a result, the inter-rater reliability for the diagnosis of MD, even under the most favorable situations, remains modest [Gibbons and Eggleston, 1996]. Although there are a number of potential pathways for resolving this thorny issue, the first factor implicit to any solution is the tacit recognition that MD is a syndrome, not a disease, and that not all facets of its etiology are equally amenable to delineation. In addition, different symptoms of the syndrome such as lack of concentration, depressed mood, and lack of energy might have biologically distinct causes. Therefore, we should look for biomarkers for specific symptom clusters and not for depression as a syndrome. Second, strong consideration should be given to research designs that incorporate non-self-report criteria. Biologists dissecting the genetics of tobacco use disorders also encountered the phenomenon of unreliable diagnoses. In particular, the criteria contained within the standard diagnostic instrument of the past 15 years, DSM-IV, included several items that required introspection and self-report [American Psychiatric Association, 1994]. As a result, and despite the obvious ludicrousness of some of the negative self-reports (e.g., well-educated smokers denying knowledge that smoking had adverse health effects), many heavy lifetime smokers were not classified as nico-
231 tine dependent [Breslau et al., 2001]. Two workarounds for this problem have been used. First, for those researchers interested in understanding the psychological and physiological bases of smoking that are directly related to the construct of nicotine dependence, emphasis was placed on the use of the Fagerstrom test nicotine dependence scale (FTND) [Heatherton et al., 1991]. For those pursuing a more reductionist approach, selfreport of cigarette use or still better yet, objective assessment of cigarette use via cotinine determinations, or cigarette counts was employed. Applying these lessons to MD will not be a straightforward process and it is important to recognize that neither nicotine dependence nor cigarette smoking is a clinical diagnosis in the latest iteration of the DSM [American Psychiatric Association, 2013]. In addition, the ascertainment of smoking status in the aforementioned studies benefited from the use of existing biomarkers (e.g., cotinine). Nevertheless, one solution that takes advantage of these lessons may be to embrace the spirit of some of the proposals enunciated in the controversial research domain criteria (RDOCs) proposal [Windsor et al., 1993], and focus only on those subjects for whom there is some semblance of changing biomarker status (e.g., changes in free thyroxine or cortisol levels) or whose behavior can be quantified using recently developed “people tracker” techniques [Rizzo et al., 2007].
Confounding Environmental Factors It is highly important in the field of research on psychiatric disorders such as MD to consider not only genetic but also environmental factors and gene environmental interaction effects (G E) in disorder etiology. The seminal work of [Caspi et al., 2002], who showed that serotonin transporter (SLC6A4) genotype interacts with the number of stressful life events of New Zealand teenagers to influence their subsequent risk of developing MD, beautifully demonstrated the importance of both genetic and environmental factors in MD. Similarly, [Kolassa et al., 2010] demonstrated that the lifetime risk for post-traumatic stress disorder (PTSD), a construct with many similar attributes to MD, exhibits an interaction at this very same SLC6A4 polymorphism with a radically different set of environmental circumstancesgenocide. These studies suggest that genetic factors might play a larger role under conditions of low/moderate stress/trauma load, while they lose significance under conditions of extremely high stress/trauma load. However, reconciling or equating the often very different types of trauma or stress load over the lifetime in various studies is extremely difficult and presents challenges to investigators seeking to operationalize these studies in larger data sets. A good example of this difficulty is the continuing controversy with respect to one of the most heavily studied genetic variants in the genome, the serotonin transporter polymorphism (5HTTLPR). The results from several independent meta-analyses led investigators to strongly differing conclusions with respect to the effect size of G E effects, in spite of using similar meta-analytic techniques and largely overlapping data sets [Risch et al., 2009; Karg et al., 2011]. Since this polymorphism is perhaps the leading candidate variant for differential susceptibility to MD, addressing this problem is of utmost importance.
232 The environmentally contextual influence of genetic factors has been a critical focus of the substance use community for one very clear reason: unlike MD, substance use can only occur if the given substance is present in the environment. Selecting the best method for quantifying the critical portions of the risk environment for tobacco use disorders is still a problem for geneticists investigating tobacco use. Some geneticists have successfully used a threshold of the lifetime smoking of 100 cigarettes as the critical denominator for adequate cigarette exposure for the elimination of some loci involved in nicotine dependence [Bierut et al., 2008]. This is by no means a perfect solution and the use of this somewhat arbitrary self-report criterion excludes many potential subjects who have not been exposed to 100 cigarettes from inclusion in studies. However, it has been an effective approach in identifying loci for nicotine dependence. Applying exposure threshold criteria in the search of biomarkers for MD will also not be easy. For many syndromes, the presence or absence of critical environmental variables such as smoke or allergens can be readily determined in the clinical setting. For MD, this is not readily done. While it is possible to document and quantitate exposure to extreme psychosocial stressors, such as genocide, in unique research populations [Boyd et al., 1998], in general, the retrospective assessment of routine psychosocial stressors in most clinical settings is unreliable for a variety of social and legal reasons. However, there are alternative approaches. For example, socioeconomic status (SES) is a well-established risk factor for MD [Cutrona et al., 2005]. The use of census data and geographic methods has proven extremely useful in previous biological investigations of MD, through identifying the presence of high rates of established stressors such as poverty, crime, and racism in focal populations. Due to their inherent scalability, such approaches should be enormously beneficial for large epidemiological studies of MD.
Attention to Proband Age A third critical problem has been the broad range of proband age employed in many genetic studies. Although often ignored, twin studies of MD have consistently shown that the heritable diathesis for MD exerts its most prominent effects between the ages of 16 and 30 [Kendler et al., 2009]. After the age of 40, the presence of medical comorbidities, such as type 2 diabetes mellitus and coronary artery disease, begins to exert their well-established effects on vulnerability to MD [Mezuk et al., 2008; Kendler et al., 2009; Su et al., 2009]. In attempts to harvest clinical information about life events during earlier time periods from older subjects, many investigative teams have used lifetime structured diagnostic assessments. Unfortunately, it is well-established that recall of critical events that occurred in the distant past is inherently flawed. Furthermore, because of the strong influence of comorbid illnesses, especially substance use, on critical physiologic and epigenetic characteristics, the use of bio-specimens from older subjects for the identification of non-genetic biomarkers is highly suspect. This problem of age dependent effects is also encountered in the study of tobacco use disorders. As in the case for MD, the effects of the heritable diatheses for nicotine dependence also wane after the age of 35 [Lessov et al., 2004]. This is particularly true at the 15q13
AMERICAN JOURNAL OF MEDICAL GENETICS PART B
FIG. 1. The relationship between smoking and (1) lymphocyte DNA methylation at AHRR CpG residue cg05575921 and (2) total number of significant changes in genome wide methylation as a function of age of the subject (blue numbers). The total sample sizes in the cohorts are: for the 19 years old subjects, n ¼ 181, for the 22 years old subjects, n ¼ 107, for the 50 years old subjects, n ¼ 111 [Philibert et al., 2012b].
locus containing variability for nicotine dependence. In younger cohorts, particularly when looking at nicotine dependence, the relative risk of variants is substantial (e.g., 1.5) [Flay, 1985]. However, when lumping all smokers together, particularly older smokers, the effect size of the variant markedly diminishes [Owen and McNeill, 2001]. Age-dependent changes are also seen in epigenetic studies. While in younger smokers changes in DNA methylation are small and are limited to a small number of critical loci, the changes in the DNA methylation signatures associated with smoking in older subjects are profound, affecting large numbers of loci. Figure 1 illustrates the changes in methylation both at a candidate biomarker for smoking (AHRR residue cg05575921) and genome wide, in association with smoking as a function of age, using the data from three of our recent studies [Philibert et al., 2012a, 2013; Dogan et al. in preparation]. As the figure illustrates, older smokers have a greater degree of change at cg05575921 than younger smokers, presumably as a result of their greater daily nicotine consumption, and many more years of exposure. In addition, the number of other loci affected increases exponentially. In total, 0.5% of all the investigated loci (2,658 of the 485,577 CpG residues) show evidence of epigenetic remodeling at genome wide significance levels. Because nicotine dependence is also associated with MD, the potential for the confounding of studies of MD by older smoking subjects is substantial [Tsuang et al., 2012]. What is more, this strong degree of change of genomic tone in association with substance use is not limited to smoking but is found in association with the use of other substances such alcohol [Manzardo et al., 2012; Philibert et al., 2012b]. Conceptually, the solution to this problem is straightforward: only include subjects older than 16 and younger than 35, while excluding those with severe substance use syndromes. Unfortunately, this would preclude large portions of the population from
PHILIBERT ET AL. participating in certain large-scale examinations of the biology of MD. But this critique presumes that future studies of the biology of MD will use traditional cladistic approaches for subject recruitment and characterization. In the current research environment, which recognizes the multiplicity of pathways to the common outcome that is MD, strict cladistic atheoretical approaches are unlikely. Indeed, it very well may be that the inclusion of even more additional inclusion criteria, such as selection for exposure to a particular stressor such as severe child abuse, may be necessary to identify genetic or physiological pathways that are unique to a given presentation of MD.
The Path Forward The three roadblocks outlined above are but a fraction of the challenges facing modern-day investigative efforts to uncover reliable biomarkers of MD. They are focused on the problems encountered in the analysis of data obtained from humans and the three solutions that have been proposed will not fully fix the problems. In addition, our discussion should not be construed as the minimization of a potential role for animal studies in these efforts. Indeed, several highly touted integrated studies of peripheral biomarkers for MD in rodent and humans have been recently published [Pajer et al., 2012; Guintivano et al., 2013]. However, as of yet, these studies have not been replicated and we refer the reader elsewhere for a thoughtful discussion of the problems encountered in the modeling of MD and other neuropsychiatric disorders in rodents [Nestler and Hyman, 2010]. These roadblocks are not unique to the search for biomarkers of MD and are in fact shared throughout the field of the biology of behavior. Also, they are not secondary to a lack of effort by investigators. But it is important to recognize the existence of these impediments in order to minimize the waste of personnel and economic resources. If true, the implications of these points are profound. By direct implication, the utility of many existing biomaterial collections for future genetic and epigenetic studies of the biology of MD may be limited. Furthermore, the recommendations swim against the prevailing tide of behavioral genetics, and suggest that bigger data sets may not necessarily be better data sets, and that efforts to contain the costs of phenotypic assessments for MD may markedly diminish the value of the resulting data sets. Indeed, we believe that the pathway to the identification of robust biomarkers for MD will necessitate large-scale, sociologically, and psychologically informed investigations of validated biological constructs, or endophenotypes that may overlap only partially with the current clinical spectrum of MD as defined by the DSM. In addition, they should take advantage of unfortunate, yet naturally occurring processes such as severe socioeconomic strife to capture the biology of the phenotypic extremes. Admittedly, these new proposed efforts may only address a small fraction of the current economic burden posed by MD. These efforts will also be costly in both time and materials. But the current approaches are simply not working. As a popular quote attributed to Albert Einstein notes, the definition of insanity is “doing the same thing over and over again and expecting different results.” In the quest to end insanity, let us try a more sanguine approach.
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ACKNOWLEDGMENTS This work is supported by grants MH080898 and DA034457 to Dr. Philibert. Dr. Philibert is the Chief Scientific Officer and majority stockholder of Behavioral Diagnostics, Inc. (www.bdmethylation. com). He is also a co-owner of U.S Patent 8,637,652, which covers the use of methylation at cg0557921 for the ascertainment of smoking status. Finally, he is also co-owner of a number of pending intellectual property rights claims with respect to the use of methylation for the ascertainment of cannabis, tobacco, and alcohol use status. HG is supported by funding from the Deutsche Forschungsgemeinschaft and the Zukunftskolleg at the University of Konstanz. Dr. Kolassa has received funding from the European commission (Competitiveness and Innovation Framework Programme, CIP), the German Research Foundation (DFG), the Federal Ministry of Education and Research (BMBF), and the Heidelberg Academy of Sciences (WIN Kolleg).
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The search for Peripheral Biomarkers for Major Depression: Benefiting from Successes in the Biology of Smoking Robert Philibert,1,2* Helen M. Gunter,2,3 and Iris-Tatjana Kolassa2,4 1
Department of Psychiatry, University of Iowa, Iowa City, Iowa
2
Zukunftskolleg, University of Konstanz, Konstanz, Germany Department of Biology, University of Konstanz, Konstanz, Germany
3 4
Clinical & Biological Psychology, University of Ulm, Ulm, Germany
Manuscript Received: 17 September 2013; Manuscript Accepted: 29 January 2014
The search for robust, clinically useful markers for major depression (MD) has been relatively unproductive. This is unfortunate because MD is one of the largest socio-economic challenges for much of the world and the development of reliable biomarkers for MD could aid in the prevention or treatment of this common syndrome. In this editorial, we compare the approaches taken in the search for biomarkers for MD to that of the more successful searches for biomarkers for tobacco use, and identify several substantive barriers. We suggest that many of the existing clinical repositories used in these biomarkers searches for MD may be of limited value. We conclude that in the future greater attention should be given to the clinical definitions, characterization of confounding environmental factors and age of subjects included in studies. Ó 2014 Wiley Periodicals, Inc.
Key words: substance use; major depression; biomarker
INTRODUCTION The search for clinically useful biological markers (biomarkers) for major depression (MD) has, to date, been relatively unproductive [Schmidt et al., 2011; Sim and Ingelman-Sundberg, 2011]. Despite the investment of considerable intellectual and economic resources, many feel that we have exhausted all possible leads, and that the scientific quest to identify biomarkers for MD is nearing an unsuccessful conclusion. This is an unfortunate conclusion because in the 21st century, MD has escalated from an intellectual curiosity to a profound socioeconomic challenge. Over the lifetime, MD affects nearly one in four Americans. From an economic perspective, by 2030 it will be the 2nd largest economic burden worldwide [Sasayama et al., 2011]. From the personal perspective, suicide, an all-too-frequent outcome, is the 10th leading cause of death in the United States [Murphy et al., 2012]. In response to that challenge, a number of pharmacological and cognitive/behavioral treatment paradigms for MD have been developed and empirically validated in research settings. However,
Ó 2014 Wiley Periodicals, Inc.
How to Cite this Article: Philibert R, Gunter HM, Kolassa I-T. 2014. The search for peripheral biomarkers for major depression: Benefiting from successes in the biology of smoking. Am J Med Genet Part B 165B:230–234.
when these treatments are generalized to the wider population, their effectiveness rapidly diminishes––sometimes to the point where their effects are not discernible [Kirsch et al., 2008]. In the current healthcare environment, which stresses the necessity of using evidence-based treatment paradigms in order to be reimbursed, the near absence of generalizable effects for some of these treatments raises questions with respect to the validity of the diagnostic criteria and the need to reimburse psychiatric treatment. The development of biomarkers for MD, or more adroitly focusing on MD-treatment responsiveness would address many Conflict of interest: Dr. Philibert is supported by R01MH080789 and R21DA034457. He is the Chief Scientific Officer for Behavioral Diagnostics whose product focus is epigenetic markers for substance use and is both an owner and a royalty recipient of granted and pending patents for the use of DNA methylation to diagnose substance use. Grant sponsor: Deutsche Forschungsgemeinschaft and the Zukunftskolleg at the University of Konstanz; Grant sponsor: European commission (Competitiveness and Innovation Framework Programme, CIP); Grant sponsor: German Research Foundation (DFG); Grant sponsor: Federal Ministry of Education and Research (BMBF); Grant sponsor: Heidelberg Academy of Sciences (WIN Kolleg). Correspondence to: Robert Philibert, M.D., Ph.D., Department of Psychiatry, University of Iowa, Room 2-126b, Psychiatry Research/MEB, Iowa City, Iowa 52242. E-mail: [email protected] Article first published online in Wiley Online Library (wileyonlinelibrary.com): 4 March 2014 DOI 10.1002/ajmg.b.32227
230
PHILIBERT ET AL. of these issues. In particular, the development of the latter would improve the health care of millions of individuals by; (1) helping to identify sub-syndromal cases that would otherwise fester untreated, (2) allowing for the exclusion from treatment of those individuals who are unlikely to benefit from treatment, (3) contributing to efforts to better prevent MD, and (4) providing a tool to better understand the phenomenology and biology of non-treatment responsive MD. Therefore, there is a substantial rationale for MD biomarker development. Conceivably, useful biomarkers for MD could be developed through genetic, epigenetic, serological, or imaging approaches. Unfortunately, at the current time, several lengthy, authoritative reviews of the subject have concluded that none of these approaches have resulted in the generation of clinically useful biomarkers for either categorical MD or MD-treatment response. These reviews also do not suggest a set of mechanisms through which this knowledge deficit can be rectified. In this editorial, using valuable insights gained from the searches for biomarkers for tobacco use disorders, we identified three crucial roadblocks and suggest a set of concrete recommendations to improve current efforts to identify peripheral biomarkers for MD.
BARRIERS TO THE DEVELOPMENT OF BIOMARKERS FOR MD AND THEIR POTENTIAL WORKAROUNDS Unreliable Diagnosis Although there have been a large number of potential hurdles for the development of robust biomarkers for MD, perhaps the most severe has been the unreliability of MD diagnosis. To a certain extent, this is a circular argument and the clinical diagnosis of MD has rested largely on self-report data. Unfortunately, a large number of behavioral studies have shown that self-report data for affective symptoms can be unreliable [Wittkampf et al., 2007]. As a result, the inter-rater reliability for the diagnosis of MD, even under the most favorable situations, remains modest [Gibbons and Eggleston, 1996]. Although there are a number of potential pathways for resolving this thorny issue, the first factor implicit to any solution is the tacit recognition that MD is a syndrome, not a disease, and that not all facets of its etiology are equally amenable to delineation. In addition, different symptoms of the syndrome such as lack of concentration, depressed mood, and lack of energy might have biologically distinct causes. Therefore, we should look for biomarkers for specific symptom clusters and not for depression as a syndrome. Second, strong consideration should be given to research designs that incorporate non-self-report criteria. Biologists dissecting the genetics of tobacco use disorders also encountered the phenomenon of unreliable diagnoses. In particular, the criteria contained within the standard diagnostic instrument of the past 15 years, DSM-IV, included several items that required introspection and self-report [American Psychiatric Association, 1994]. As a result, and despite the obvious ludicrousness of some of the negative self-reports (e.g., well-educated smokers denying knowledge that smoking had adverse health effects), many heavy lifetime smokers were not classified as nico-
231 tine dependent [Breslau et al., 2001]. Two workarounds for this problem have been used. First, for those researchers interested in understanding the psychological and physiological bases of smoking that are directly related to the construct of nicotine dependence, emphasis was placed on the use of the Fagerstrom test nicotine dependence scale (FTND) [Heatherton et al., 1991]. For those pursuing a more reductionist approach, selfreport of cigarette use or still better yet, objective assessment of cigarette use via cotinine determinations, or cigarette counts was employed. Applying these lessons to MD will not be a straightforward process and it is important to recognize that neither nicotine dependence nor cigarette smoking is a clinical diagnosis in the latest iteration of the DSM [American Psychiatric Association, 2013]. In addition, the ascertainment of smoking status in the aforementioned studies benefited from the use of existing biomarkers (e.g., cotinine). Nevertheless, one solution that takes advantage of these lessons may be to embrace the spirit of some of the proposals enunciated in the controversial research domain criteria (RDOCs) proposal [Windsor et al., 1993], and focus only on those subjects for whom there is some semblance of changing biomarker status (e.g., changes in free thyroxine or cortisol levels) or whose behavior can be quantified using recently developed “people tracker” techniques [Rizzo et al., 2007].
Confounding Environmental Factors It is highly important in the field of research on psychiatric disorders such as MD to consider not only genetic but also environmental factors and gene environmental interaction effects (G E) in disorder etiology. The seminal work of [Caspi et al., 2002], who showed that serotonin transporter (SLC6A4) genotype interacts with the number of stressful life events of New Zealand teenagers to influence their subsequent risk of developing MD, beautifully demonstrated the importance of both genetic and environmental factors in MD. Similarly, [Kolassa et al., 2010] demonstrated that the lifetime risk for post-traumatic stress disorder (PTSD), a construct with many similar attributes to MD, exhibits an interaction at this very same SLC6A4 polymorphism with a radically different set of environmental circumstancesgenocide. These studies suggest that genetic factors might play a larger role under conditions of low/moderate stress/trauma load, while they lose significance under conditions of extremely high stress/trauma load. However, reconciling or equating the often very different types of trauma or stress load over the lifetime in various studies is extremely difficult and presents challenges to investigators seeking to operationalize these studies in larger data sets. A good example of this difficulty is the continuing controversy with respect to one of the most heavily studied genetic variants in the genome, the serotonin transporter polymorphism (5HTTLPR). The results from several independent meta-analyses led investigators to strongly differing conclusions with respect to the effect size of G E effects, in spite of using similar meta-analytic techniques and largely overlapping data sets [Risch et al., 2009; Karg et al., 2011]. Since this polymorphism is perhaps the leading candidate variant for differential susceptibility to MD, addressing this problem is of utmost importance.
232 The environmentally contextual influence of genetic factors has been a critical focus of the substance use community for one very clear reason: unlike MD, substance use can only occur if the given substance is present in the environment. Selecting the best method for quantifying the critical portions of the risk environment for tobacco use disorders is still a problem for geneticists investigating tobacco use. Some geneticists have successfully used a threshold of the lifetime smoking of 100 cigarettes as the critical denominator for adequate cigarette exposure for the elimination of some loci involved in nicotine dependence [Bierut et al., 2008]. This is by no means a perfect solution and the use of this somewhat arbitrary self-report criterion excludes many potential subjects who have not been exposed to 100 cigarettes from inclusion in studies. However, it has been an effective approach in identifying loci for nicotine dependence. Applying exposure threshold criteria in the search of biomarkers for MD will also not be easy. For many syndromes, the presence or absence of critical environmental variables such as smoke or allergens can be readily determined in the clinical setting. For MD, this is not readily done. While it is possible to document and quantitate exposure to extreme psychosocial stressors, such as genocide, in unique research populations [Boyd et al., 1998], in general, the retrospective assessment of routine psychosocial stressors in most clinical settings is unreliable for a variety of social and legal reasons. However, there are alternative approaches. For example, socioeconomic status (SES) is a well-established risk factor for MD [Cutrona et al., 2005]. The use of census data and geographic methods has proven extremely useful in previous biological investigations of MD, through identifying the presence of high rates of established stressors such as poverty, crime, and racism in focal populations. Due to their inherent scalability, such approaches should be enormously beneficial for large epidemiological studies of MD.
Attention to Proband Age A third critical problem has been the broad range of proband age employed in many genetic studies. Although often ignored, twin studies of MD have consistently shown that the heritable diathesis for MD exerts its most prominent effects between the ages of 16 and 30 [Kendler et al., 2009]. After the age of 40, the presence of medical comorbidities, such as type 2 diabetes mellitus and coronary artery disease, begins to exert their well-established effects on vulnerability to MD [Mezuk et al., 2008; Kendler et al., 2009; Su et al., 2009]. In attempts to harvest clinical information about life events during earlier time periods from older subjects, many investigative teams have used lifetime structured diagnostic assessments. Unfortunately, it is well-established that recall of critical events that occurred in the distant past is inherently flawed. Furthermore, because of the strong influence of comorbid illnesses, especially substance use, on critical physiologic and epigenetic characteristics, the use of bio-specimens from older subjects for the identification of non-genetic biomarkers is highly suspect. This problem of age dependent effects is also encountered in the study of tobacco use disorders. As in the case for MD, the effects of the heritable diatheses for nicotine dependence also wane after the age of 35 [Lessov et al., 2004]. This is particularly true at the 15q13
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FIG. 1. The relationship between smoking and (1) lymphocyte DNA methylation at AHRR CpG residue cg05575921 and (2) total number of significant changes in genome wide methylation as a function of age of the subject (blue numbers). The total sample sizes in the cohorts are: for the 19 years old subjects, n ¼ 181, for the 22 years old subjects, n ¼ 107, for the 50 years old subjects, n ¼ 111 [Philibert et al., 2012b].
locus containing variability for nicotine dependence. In younger cohorts, particularly when looking at nicotine dependence, the relative risk of variants is substantial (e.g., 1.5) [Flay, 1985]. However, when lumping all smokers together, particularly older smokers, the effect size of the variant markedly diminishes [Owen and McNeill, 2001]. Age-dependent changes are also seen in epigenetic studies. While in younger smokers changes in DNA methylation are small and are limited to a small number of critical loci, the changes in the DNA methylation signatures associated with smoking in older subjects are profound, affecting large numbers of loci. Figure 1 illustrates the changes in methylation both at a candidate biomarker for smoking (AHRR residue cg05575921) and genome wide, in association with smoking as a function of age, using the data from three of our recent studies [Philibert et al., 2012a, 2013; Dogan et al. in preparation]. As the figure illustrates, older smokers have a greater degree of change at cg05575921 than younger smokers, presumably as a result of their greater daily nicotine consumption, and many more years of exposure. In addition, the number of other loci affected increases exponentially. In total, 0.5% of all the investigated loci (2,658 of the 485,577 CpG residues) show evidence of epigenetic remodeling at genome wide significance levels. Because nicotine dependence is also associated with MD, the potential for the confounding of studies of MD by older smoking subjects is substantial [Tsuang et al., 2012]. What is more, this strong degree of change of genomic tone in association with substance use is not limited to smoking but is found in association with the use of other substances such alcohol [Manzardo et al., 2012; Philibert et al., 2012b]. Conceptually, the solution to this problem is straightforward: only include subjects older than 16 and younger than 35, while excluding those with severe substance use syndromes. Unfortunately, this would preclude large portions of the population from
PHILIBERT ET AL. participating in certain large-scale examinations of the biology of MD. But this critique presumes that future studies of the biology of MD will use traditional cladistic approaches for subject recruitment and characterization. In the current research environment, which recognizes the multiplicity of pathways to the common outcome that is MD, strict cladistic atheoretical approaches are unlikely. Indeed, it very well may be that the inclusion of even more additional inclusion criteria, such as selection for exposure to a particular stressor such as severe child abuse, may be necessary to identify genetic or physiological pathways that are unique to a given presentation of MD.
The Path Forward The three roadblocks outlined above are but a fraction of the challenges facing modern-day investigative efforts to uncover reliable biomarkers of MD. They are focused on the problems encountered in the analysis of data obtained from humans and the three solutions that have been proposed will not fully fix the problems. In addition, our discussion should not be construed as the minimization of a potential role for animal studies in these efforts. Indeed, several highly touted integrated studies of peripheral biomarkers for MD in rodent and humans have been recently published [Pajer et al., 2012; Guintivano et al., 2013]. However, as of yet, these studies have not been replicated and we refer the reader elsewhere for a thoughtful discussion of the problems encountered in the modeling of MD and other neuropsychiatric disorders in rodents [Nestler and Hyman, 2010]. These roadblocks are not unique to the search for biomarkers of MD and are in fact shared throughout the field of the biology of behavior. Also, they are not secondary to a lack of effort by investigators. But it is important to recognize the existence of these impediments in order to minimize the waste of personnel and economic resources. If true, the implications of these points are profound. By direct implication, the utility of many existing biomaterial collections for future genetic and epigenetic studies of the biology of MD may be limited. Furthermore, the recommendations swim against the prevailing tide of behavioral genetics, and suggest that bigger data sets may not necessarily be better data sets, and that efforts to contain the costs of phenotypic assessments for MD may markedly diminish the value of the resulting data sets. Indeed, we believe that the pathway to the identification of robust biomarkers for MD will necessitate large-scale, sociologically, and psychologically informed investigations of validated biological constructs, or endophenotypes that may overlap only partially with the current clinical spectrum of MD as defined by the DSM. In addition, they should take advantage of unfortunate, yet naturally occurring processes such as severe socioeconomic strife to capture the biology of the phenotypic extremes. Admittedly, these new proposed efforts may only address a small fraction of the current economic burden posed by MD. These efforts will also be costly in both time and materials. But the current approaches are simply not working. As a popular quote attributed to Albert Einstein notes, the definition of insanity is “doing the same thing over and over again and expecting different results.” In the quest to end insanity, let us try a more sanguine approach.
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ACKNOWLEDGMENTS This work is supported by grants MH080898 and DA034457 to Dr. Philibert. Dr. Philibert is the Chief Scientific Officer and majority stockholder of Behavioral Diagnostics, Inc. (www.bdmethylation. com). He is also a co-owner of U.S Patent 8,637,652, which covers the use of methylation at cg0557921 for the ascertainment of smoking status. Finally, he is also co-owner of a number of pending intellectual property rights claims with respect to the use of methylation for the ascertainment of cannabis, tobacco, and alcohol use status. HG is supported by funding from the Deutsche Forschungsgemeinschaft and the Zukunftskolleg at the University of Konstanz. Dr. Kolassa has received funding from the European commission (Competitiveness and Innovation Framework Programme, CIP), the German Research Foundation (DFG), the Federal Ministry of Education and Research (BMBF), and the Heidelberg Academy of Sciences (WIN Kolleg).
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